{"as_of":"2026-08-16T14:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b93a04ae43f3d65efb02ee569273156c2d5436a62b2bd00c9549f7bcff98a9e8","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:19:55.972977Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.03369/citation-record","integrity":"/paper/1908.03369/integrity","json":"/paper/1908.03369/citation-record.json","paper":"/paper/1908.03369"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.927564Z","title":"Deepdriving: Learning affordance for direct perception in autonomous driving,","venue":null,"work_id":"b1b615d5-471b-422f-9dfb-7dea5a379440","year":2015},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.725276Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:6cda73b19017aba867903de11976ca92515b028ece6f962a851cdbf1363e94b1","observation_id":"ba4e55f4-9ebe-4461-9971-6243d26b894a","resolution":{"observed_at":"2026-08-14T14:19:56.931665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.915487Z","title":"Medical image analysis using convolutional neural networks: a review,","venue":null,"work_id":"f1a7b768-7cc4-4a57-a37d-b0edb45c9bea","year":2018},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.731602Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:17bc0656aacac9048b7eae5d91ed348cc5b1bade35cbf7f53729ddeb67bf819a","observation_id":"2b346346-1807-4507-b3ac-4da660c6f12f","resolution":{"observed_at":"2026-08-14T14:19:56.919924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.903650Z","title":"Deepface: Closing the gap to human-level performance in face verification,","venue":null,"work_id":"989d1273-3054-4ff6-9669-812c34c599a0","year":2014},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.736516Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:8a093c3b48af3b586f6fcae72ec3983a050215b5670225f799f1caf79c5a7159","observation_id":"5d887025-aa1f-4f24-984d-fc322fc2e55e","resolution":{"observed_at":"2026-08-14T14:19:56.908077Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.890893Z","title":"Adversary resistant deep neural networks with an application to malware detection,","venue":null,"work_id":"9ab6fb0d-b612-4f43-9d06-3e4d2f8bfc36","year":2017},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.741119Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:31621055f0b24a32be3d7021e38f1bec8bb678b2d5d26328d3589f94d6e8d8c1","observation_id":"8d30b422-c720-4807-8b44-7d34341d40ed","resolution":{"observed_at":"2026-08-14T14:19:56.894975Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.876258Z","title":"Droid-sec: deep learning in android malware detection,","venue":null,"work_id":"b8c059eb-2357-4bbd-bc61-0f93b4cea714","year":2014},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.746098Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:1c8cf1f9ba126dec07e6cecdba38bf420bf94d5f176e322f5e766b64d09160c6","observation_id":"a9141273-9a4b-4730-ab8a-3b82502726da","resolution":{"observed_at":"2026-08-14T14:19:56.881535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.863047Z","title":"Badnets: Evaluating backdooring attacks on deep neural networks,","venue":null,"work_id":"4c23d5aa-34f0-43fe-8ad4-1da57cef8853","year":2019},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.750366Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:5158c06b4611c49be6dfe4f84f425e47eecb5f7a0e21b990b0ea37dd07b982ae","observation_id":"7e154007-fc81-475f-a5f0-8f2ac2a18464","resolution":{"observed_at":"2026-08-14T14:19:56.867894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.848569Z","title":"Amazon machine learning","venue":null,"work_id":"5e02c805-3ae0-4a92-8fc4-430b75e00308","year":null},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.755443Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:13bcb4df0fc4fe9028eea361b5c19f82a54b6b0b9743728b7c7d35ff92653aee","observation_id":"14cc8b56-29ed-47a2-a892-d76456165d40","resolution":{"observed_at":"2026-08-14T14:19:56.853171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.834819Z","title":"Caffe model zoo","venue":null,"work_id":"d297d637-2c13-49fc-85d0-1c4d21d9b831","year":null},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.763796Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:c06c2e3e8de564f54f1b680f0da7b873a5aaea685e8b00bdc1f0bf3df5ec8264","observation_id":"ff16fb0d-22c9-452c-ab3b-139ba955746c","resolution":{"observed_at":"2026-08-14T14:19:56.839360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.820167Z","title":"Gradientzoo: pre-trained neural network models","venue":null,"work_id":"2d12a310-e677-4933-b8d6-b7831b22496a","year":null},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.769468Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:426df6f24f29f0749e513378998ab056270ef158e2d6e571dc870b609d5c16c1","observation_id":"3a964839-a1af-43b9-8872-6b36755a5c4d","resolution":{"observed_at":"2026-08-14T14:19:56.824817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.805452Z","title":"Model zoo","venue":null,"work_id":"3b242153-bba1-4675-82af-9e2fdcb6f514","year":null},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.775410Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:feb14b6c2149ee6528083fecf6f47d763de56d4c3395da6ea15069a0d51976ec","observation_id":"52cedf56-c6ad-44c1-bf74-0536b29fd396","resolution":{"observed_at":"2026-08-14T14:19:56.810056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.790818Z","title":"Trojaning attack on neural networks,","venue":null,"work_id":"ef106369-b9b0-453b-b1d8-e8a1a6c88018","year":2018},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.780602Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:38bb6a4f598bab1da254d2ce4c4d2a1ff136ade3dc3f07337179ee8c531498a1","observation_id":"320ec28d-c189-4404-a7f4-c15162850f63","resolution":{"observed_at":"2026-08-14T14:19:56.795573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.774127Z","title":"Targeted backdoor attacks on deep learning systems using data poisoning,","venue":null,"work_id":"2c594444-4dfa-4e87-8c38-39672bfc67d4","year":2017},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.785728Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:92ebf27a505183236f11f9cfdfb7e1ac6b7f2901131bcdb072512b3ade75f383","observation_id":"a8f445f4-b8a2-4665-b589-8e472064562c","resolution":{"observed_at":"2026-08-14T14:19:56.778942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.760118Z","title":"How to backdoor federated learning,","venue":null,"work_id":"c3e64e9b-b9cd-4d8a-b4b8-9da078872f2d","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.790415Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:ad9b054fa6ff7aeb07cd4faaaab1876459a9c4b34a3cade145f3a417b048bd7d","observation_id":"cf6066ef-4141-4216-964a-b08f515e2cb3","resolution":{"observed_at":"2026-08-14T14:19:56.764146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.744179Z","title":"Evaluating the visualization of what a deep neural network has learned,","venue":null,"work_id":"b970ac2e-9303-421e-b168-852fa60a924e","year":2017},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.796130Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:c424251707db48907df0556f5cc1263f6c75d69a6e85742243f43d173bf665db","observation_id":"507844a9-4106-4e31-94a3-409fb9948114","resolution":{"observed_at":"2026-08-14T14:19:56.749689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.729634Z","title":"The challenges and opportunities of explainable ai,","venue":null,"work_id":"dbf56c76-09ab-4b2d-92ff-406c85afb78e","year":2018},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.800286Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:daa1f614b440d89e52affa17eb78b8f6573c78b7e4068555fcf4c2b59b221b41","observation_id":"4fa2ce2e-fec3-44f0-a833-e415cd1fbaa7","resolution":{"observed_at":"2026-08-14T14:19:56.734104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.716140Z","title":"Broad agency announcement for trojai","venue":null,"work_id":"0ca2a286-7478-481f-a54e-f99d15715e3e","year":null},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.805053Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:ffbb3c3225ebcd1004c99686570e9940f141174cff310cc8397612f87b3ca8af","observation_id":"5eb00b16-f52e-4f58-bfd1-179c87f48784","resolution":{"observed_at":"2026-08-14T14:19:56.720216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.701144Z","title":"Sentinet: Detecting physical attacks against deep learning systems,","venue":null,"work_id":"d7966f89-29be-415e-97c8-e44e0bd68e28","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.809588Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:b7b08d3fbfc290790d2e431c3fc0c3da80502c451ad3f722d170a09cfefadd5d","observation_id":"afeca122-3277-4dd4-874b-472db49d0d64","resolution":{"observed_at":"2026-08-14T14:19:56.707000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.684406Z","title":"Strip: A defence against trojan attacks on deep neural networks,","venue":null,"work_id":"6df0bed5-c61d-478a-88bd-85ed5129b174","year":2019},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.814831Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:3ac707c46b623429744d175f31da6b05ea2fe462ca5f9e79811bf80c6d590380","observation_id":"537c2172-6f89-4d78-8816-1497b2f5d60b","resolution":{"observed_at":"2026-08-14T14:19:56.690272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.666843Z","title":"Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,","venue":null,"work_id":"8cb3aa78-594b-41fc-a15b-611138398687","year":2019},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.818888Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:c19187107ccfcc40116e59d4b2ab6f7e08dbe56793d486cf018fd252799a728a","observation_id":"7e9a0122-77c6-4fb3-87b6-c10c8c81e71b","resolution":{"observed_at":"2026-08-14T14:19:56.671890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.650690Z","title":"Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks,","venue":null,"work_id":"17033f33-b7af-4f69-a3f7-e35a8bd7f24a","year":2019},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.823887Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:fa58156e1fde7daa03dced90d5c19fe5d7d77aa9912e92b6a63ab393925a7dbc","observation_id":"0aff7b61-364f-446d-9eee-110f67a6dd90","resolution":{"observed_at":"2026-08-14T14:19:56.655925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.635579Z","title":"Tabor: A highly accurate approach to inspecting and restoring trojan backdoors in ai systems,","venue":null,"work_id":"c5d05466-3322-4765-b6af-cbf9977ef91a","year":2019},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.828145Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:1094c397c9fd17094310cf4f817c4d6c1cfeab86a83b901099770aecdcb7333b","observation_id":"8a625fb0-c8c3-48ad-bb19-87b6bfbb04b6","resolution":{"observed_at":"2026-08-14T14:19:56.639995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.620693Z","title":"Fine-pruning: Defending against backdooring attacks on deep neural networks,","venue":null,"work_id":"cf389609-9ab1-4f7b-a847-6858b2b51bfb","year":2018},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.832998Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:b2232cb45597d5954ea1f9150b184ea14980e58e610dcd6fe865a9175e86d7d9","observation_id":"e23173a9-a8d2-481d-993d-317414055ba0","resolution":{"observed_at":"2026-08-14T14:19:56.626109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.603506Z","title":"Visualizing data using t-SNE,","venue":null,"work_id":"52abbe96-1c7a-4a21-bead-f86a0ff7d938","year":2008},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.837179Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:d7202d207b664b5493317859e895035b78d0e9f6806ca7652bc34f7274f57e3e","observation_id":"1627980f-a7da-4ced-a6b1-38d730ca0ef3","resolution":{"observed_at":"2026-08-14T14:19:56.608055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:55.842159Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.842159Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:93786ad6154af5635910117557fae52381ac07afb496356d98102005b68e9de0","observation_id":"eb07366a-8af7-4009-b74b-6b17d7a96a8e","resolution":{"observed_at":"2026-08-14T14:19:55.842159Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.575599Z","title":"ABS: Scanning neural networks for back-doors by artificial brain stimula- tion,","venue":null,"work_id":"577cb2bb-4973-475e-a65b-525d607e0c37","year":2019},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.846554Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:aa220ec90ab89b2534ef690e3dcfd73ed8d95e97922f4d9d68d36f42c9ce03b6","observation_id":"9167a456-cc18-4873-a808-2cc3ed9daff4","resolution":{"observed_at":"2026-08-14T14:19:56.581084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.558401Z","title":"Grad-cam: Visual explanations from deep networks via gradient-based localization,","venue":null,"work_id":"da900d71-1c3a-4782-8e76-2697a0a094c8","year":2017},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.851386Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:5316a765130b86d0c3da32052a7be0cd49e32b09e4d9ed024a668aff58c7bd90","observation_id":"8ed42c37-c64d-46c2-8e8f-3f54f9fcbff3","resolution":{"observed_at":"2026-08-14T14:19:56.563237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.543557Z","title":"Generative adversarial nets,","venue":null,"work_id":"360bb5ab-6f3f-48fb-90eb-096334e6a2e9","year":2014},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.855619Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:912d7ac2cc55b3a37ed1907b3cd437e432627876c43c34c9c33d73c22198fd94","observation_id":"e15210b0-1ca0-414a-b574-58861e9bfdb1","resolution":{"observed_at":"2026-08-14T14:19:56.547896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.528619Z","title":"Globally and locally consistent image completion,","venue":null,"work_id":"e1e82f83-64a1-4c3c-b3fa-03f942cfff47","year":2017},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.859740Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:daab25429dbed4209fba94bfe337313b6de395aacaaacdb2844e0c1f47ba5187","observation_id":"09325915-5098-42c8-a62f-1acca61fb789","resolution":{"observed_at":"2026-08-14T14:19:56.534026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.513541Z","title":"Improved training of wasserstein gans,","venue":null,"work_id":"caabcc0a-3d44-4fda-bbf5-aff6eac0f1f4","year":2017},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.864279Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:d232314335002fe214a4ee36be027ce5c216d0a15739a7c8dd8c87d4e997802f","observation_id":"3360218d-7d80-40c9-ae6e-b4972bdaf981","resolution":{"observed_at":"2026-08-14T14:19:56.518515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:55.869036Z","title":"Learning multiple layers of features from tiny images,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.869036Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:fffce6b465cc7756bed61fb2f3f55811a18331561b6de60e0cea975e386f6fb2","observation_id":"95a4d28e-0aa8-4e4a-a977-827bff541f73","resolution":{"observed_at":"2026-08-14T14:19:55.869036Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.486902Z","title":"Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,","venue":null,"work_id":"a3175c81-7084-4660-959a-0d000dd7ee0f","year":2012},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.872833Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:df3d43111447512af7c7caf23061ac057fbcb84263b2059d63e6fb71fa5e84aa","observation_id":"d3d8c2a4-28db-427f-99c7-c900e564cd81","resolution":{"observed_at":"2026-08-14T14:19:56.492027Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.466631Z","title":"Traffic sign recognition how far are we from the solution?","venue":null,"work_id":"59630825-a8d3-45b0-9bd4-247f6a7defe4","year":2013},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.877441Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:6f8274d2e4f069aa1f94d30fb3805354ef3a89a7da0d70aaf6c579c346b95bb5","observation_id":"cb2f26c8-8710-4d44-8ae3-dbc24f4d8d6c","resolution":{"observed_at":"2026-08-14T14:19:56.472541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.448943Z","title":"Vggface2: A dataset for recognising faces across pose and age,","venue":null,"work_id":"a02c6117-5d69-4ab8-9dbd-3e897d19d4a3","year":2018},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.882845Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:1aa5f699bfba085e50ef2fd5e1903cddc3ee10e279b4f44f20797cc84186a553","observation_id":"84d58bde-51f1-45e4-b41b-da244a5016ee","resolution":{"observed_at":"2026-08-14T14:19:56.454304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:55.887121Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.887121Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:d95538cd4334a1d39ef8a7ea5b3d9802ab3ce6c09b9ac1a629879da5378aba84","observation_id":"fe8acefe-30a0-409f-bea1-3adc18388039","resolution":{"observed_at":"2026-08-14T14:19:55.887121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:55.891345Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.891345Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:27b0133aee9643735edcd0efef43fffdd73149116bc935550e3826b8fe42fdd7","observation_id":"03df75d2-e843-4ea9-a473-3fdbd0dc32f2","resolution":{"observed_at":"2026-08-14T14:19:55.891345Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.415885Z","title":"Deep face recognition,","venue":null,"work_id":"3c64f948-2d1b-447b-baf3-a69cb726783c","year":2015},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.896251Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:eeee4c9e5e3ef31614bd61e81442565d272bc212be323d610588fbdc6efc679e","observation_id":"1cde6e2d-9720-49b4-92f5-5eb54d446016","resolution":{"observed_at":"2026-08-14T14:19:56.420211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.401119Z","title":"Interpretable deep learning under fire,","venue":null,"work_id":"1ff04223-ff8e-46df-97be-d31d3fff5562","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.900427Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:b7d5ffb8b1faae72abef5831ec314de2da344237721bbb78516570665faec99b","observation_id":"dde62024-cf14-4d5d-9ebb-6e78d0abdc05","resolution":{"observed_at":"2026-08-14T14:19:56.405838Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.386022Z","title":"Explaining and harness- ing adversarial examples,","venue":null,"work_id":"dd8a5921-e1e0-4a94-8335-cf15d05ec153","year":2015},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.905398Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:fe42cf92eadb968d39a83da4d915436b376085533cb2c294bf0995cb6a613957","observation_id":"d9950bc9-350e-4f02-9035-20873f5788c8","resolution":{"observed_at":"2026-08-14T14:19:56.391614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:55.909263Z","title":"Towards deep learning models resistant to adversarial attacks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.909263Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:914ac261f36bdc88a259cdbb80bcbc9cecd1b62f3e2e925f56b67ff1e9f2d5a4","observation_id":"046f1a48-efa1-43c7-b785-a416b662bb37","resolution":{"observed_at":"2026-08-14T14:19:55.909263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.358068Z","title":"Invisible backdoor attacks on deep neural networks via steganography and regularization,","venue":null,"work_id":"71eb26fe-64b5-4f4a-aaeb-fe26bd39464d","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.913453Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:36623e9cc761ad073497a1fadebafa35fcf478d439cf5ede7501d7a34a1c625b","observation_id":"9d4c7281-33d7-4741-a544-8c40141f74f5","resolution":{"observed_at":"2026-08-14T14:19:56.365430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.341403Z","title":"Reflection backdoor: A natural backdoor attack on deep neural networks,","venue":null,"work_id":"599e25ef-84dd-45b7-b830-aaf9ebab465a","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.918257Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:6a25bfd131ac66feffaa4d03125c81cd68284e107516cbeb0193a193ed052685","observation_id":"ad11d749-e21b-4a5b-89f1-707223c37d57","resolution":{"observed_at":"2026-08-14T14:19:56.346349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.324085Z","title":"Hidden trigger backdoor attacks,","venue":null,"work_id":"99d6236a-da71-432d-9a11-de76757aadf1","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.922444Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:1260b9d452bc801d2af6e985f6a0172a2e707b1cca43393897f1bfe6316dca35","observation_id":"01af175f-fb03-49a5-961f-6345c72a9aaf","resolution":{"observed_at":"2026-08-14T14:19:56.329304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.309637Z","title":"Blind backdoors in deep learning models,","venue":null,"work_id":"69cf9384-9ccc-42c1-89bf-13b737534314","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.927199Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:1bdc6ec79b853b5e16d8bc4c9a3c68a3f584e04c352a68942908284c2efde57e","observation_id":"ea7c1d89-9685-4032-9990-b7774ddbdef5","resolution":{"observed_at":"2026-08-14T14:19:56.314150Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.292490Z","title":"Detecting backdoor attacks on deep neural networks by activation clustering,","venue":null,"work_id":"394e88f2-09fc-42e5-8768-dd29d5332d18","year":2019},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.931563Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:74aacdd253ff460d3df3af2a7efaaa5dfab0c90bee416bc5cd8cad5b4a852499","observation_id":"84e6fc08-96ac-471f-9740-9daa1af8470b","resolution":{"observed_at":"2026-08-14T14:19:56.299113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.276764Z","title":"Design and evaluation of a multi-domain trojan detection method on deep neural networks,","venue":null,"work_id":"dbdee942-aeba-4b59-a206-3fa639334fb3","year":2019},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.937101Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:d86b88a40d645ba81ef1929f5269471e11c6f5d3e2be8e3e8226186b1a79db1c","observation_id":"c377b8c1-0382-4651-ac55-d968c1390e63","resolution":{"observed_at":"2026-08-14T14:19:56.281512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.261947Z","title":"Neural trojans,","venue":null,"work_id":"b0d343a6-959c-4c6a-98be-e5f56cafa8f0","year":2017},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.942307Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:a68fbf606d3b590e611230e27613cb66253c8a0ec7dc9877a65f8fafeed23d00","observation_id":"65780f49-23bf-45c2-b27e-10280994e515","resolution":{"observed_at":"2026-08-14T14:19:56.266638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.244097Z","title":"Backdoor attacks to graph neural networks,","venue":null,"work_id":"eb98b69c-bf66-4b07-bc46-6bebad63c70b","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.946865Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:17a25911d4e529947c8afab01df21b4854913badb7aa11cfafde8fcee9ecdff9","observation_id":"1f179079-5263-499d-95a7-13e91c7187e6","resolution":{"observed_at":"2026-08-14T14:19:56.250135Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.226734Z","title":"Rab: Provable robustness against backdoor attacks,","venue":null,"work_id":"e6829906-5858-4f8d-ab47-f788b7b09037","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.951006Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:8db5dc67fcce6a4961e1b350d4bb52115c0fdfce3bc02fb8c7f08896f2a15417","observation_id":"94685a09-06be-4d0f-b747-13ceacf4ce0b","resolution":{"observed_at":"2026-08-14T14:19:56.231609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.209246Z","title":"On certifying robustness against backdoor attacks via randomized smoothing,","venue":null,"work_id":"665c450c-5a4c-4ada-bc7c-83db68e23e7b","year":2020},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.956070Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:de31235cbafb83624dbe75331b6afd8f712b2de4329adec3960084e21b6c7d87","observation_id":"19d29d99-bd93-4be9-8157-7ab39ef313e0","resolution":{"observed_at":"2026-08-14T14:19:56.214912Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.192891Z","title":"Mnist handwritten digit database,","venue":null,"work_id":"ac859dde-826c-40b3-b921-e797cdd31a69","year":2010},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.962040Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:408212b0c7698c6a0ec906163ed2b6b55edfd44d504ffe105b209c4c3e3a1a91","observation_id":"aae74d5f-34b4-4163-9046-7d4423beeff9","resolution":{"observed_at":"2026-08-14T14:19:56.198574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.172536Z","title":"The architectural implications of autonomous driving: Constraints and acceleration,","venue":null,"work_id":"8999187c-8b5f-41ad-88f3-9a2396a49072","year":2018},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.967748Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:e4153829258dbc93322d3fd42fd11a592982d18ec6cd7d5c243bbbab44c3632a","observation_id":"fa3cf8fc-ea37-4393-9ab7-49195219b236","resolution":{"observed_at":"2026-08-14T14:19:56.178004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:19:56.152442Z","title":"Simultaneous traffic sign detection and bound- ary estimation using convolutional neural network,","venue":null,"work_id":"a1020a94-c260-47ac-8f1a-0bc32b731657","year":2018},"citing_paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","version":7},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T14:19:55.972977Z"},"links":{"citing_paper":"/paper/1908.03369"},"observation_digest":"sha256:6c0ba6f7c64a4630965544991f36b8374c222555d47c79df623b767a361b9c2d","observation_id":"fecc4d15-4c25-4fb7-8569-fea4aa5fed53","resolution":{"observed_at":"2026-08-14T14:19:56.159717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.03369","last_updated":"2020-09-28T05:34:48Z","latest_version":7,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-16T04:36:05.782689Z","submitted_at":"2019-08-09T08:51:55Z","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":47},"total_outbound_references":52},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:1908.03369."}