{"as_of":"2026-08-17T14:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b3e3d47aeaa0e9d97cffe3ca8b56ca738fd5ed5bcadffa338d6ceb2ce8f0e88","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:47:08.051733Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2507.21483/citation-record","integrity":"/paper/2507.21483/integrity","json":"/paper/2507.21483/citation-record.json","paper":"/paper/2507.21483"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T12:47:14.599216Z","title":"Audiomnist: exploring explainable artificial intelligence for audio analysis on a simple benchmark","venue":null,"work_id":"9f1e6a50-b204-4965-a18c-8e0889c9f64c","year":2024},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:04.637676Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:090f96e776ebb45228d199f3936bd29944fa67fb22ebaf9568904001d18dab0c","observation_id":"cb8f4ca3-c0e7-499f-9296-cebbcb6ab6a2","resolution":{"observed_at":"2026-08-06T12:47:14.676876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:14.383498Z","title":"Towards evaluating the robustness of neural networks","venue":null,"work_id":"ff6d328a-6604-4d8d-b323-4be0fdbcf5a8","year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:04.690630Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:8528f3c69902fb36767024c97fc01c6f75b854cbcae546a4f8a9b91bd3ea5223","observation_id":"7bd1656a-c1f9-48a9-bf95-f025df6f74ad","resolution":{"observed_at":"2026-08-06T12:47:14.529630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.03728","last_updated":"2018-11-09T01:08:00Z","snapshot_observed_at":"2026-08-16T05:42:20.700873Z","submitted_at":"2018-11-09T01:08:00Z","title":"Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.03728","snapshot_observed_at":"2026-08-06T12:47:04.755848Z","title":"Detecting backdoor attacks on deep neural networks by activation clustering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:04.755848Z"},"links":{"cited_paper":"/paper/1811.03728","citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:0c4aa12b3c0fb7d91e65fdd491b0a29f175ed03b1b76e8fbea794519b51e983b","observation_id":"580c005e-a314-44a0-8e8c-0420a40ba7ac","resolution":{"observed_at":"2026-08-06T12:47:04.755848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05526","last_updated":"2017-12-15T04:26:26Z","snapshot_observed_at":"2026-07-06T06:14:30.795326Z","submitted_at":"2017-12-15T04:26:26Z","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05526","snapshot_observed_at":"2026-08-06T12:47:04.847418Z","title":"Targeted backdoor attacks on deep learning systems using data poisoning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:04.847418Z"},"links":{"cited_paper":"/paper/1712.05526","citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:c81feb23dde236ab90c975efb59cb1daabe4469e87c99aacb97d371c277b76a7","observation_id":"e7f00ccc-d598-4dd8-8758-41d145e2a37a","resolution":{"observed_at":"2026-08-06T12:47:04.847418Z","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":"2026-08-06T12:47:14.117598Z","title":"On the detection of adaptive adversarial attacks in speaker verification systems","venue":null,"work_id":"650690a9-2505-4300-adfa-2d554145bc70","year":2023},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:04.941949Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:6b60e41c1e80f6f168dbf2162e111ad9269f2e930898e87e3fd5a039a3868b87","observation_id":"45bcc259-2538-479d-81b3-9273fb14eafb","resolution":{"observed_at":"2026-08-06T12:47:14.259451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:13.859435Z","title":"Front- end factor analysis for speaker verification","venue":null,"work_id":"e6d602d2-09be-476c-816f-91f37b6b049a","year":2010},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.016018Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:760cbf2103fa0495c8b00619a8b8654b655dae4d888f906536e452ece63cfb03","observation_id":"5866b18f-5bb7-4bcc-b6fb-22434eb8d2ca","resolution":{"observed_at":"2026-08-06T12:47:13.993344Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:05.083415Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.083415Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:75896dc4621e2581886d3fb75b235786f861511f9fd5d20385e8d552fb98a298","observation_id":"9b48ff91-eb56-4605-8498-597093bf40e4","resolution":{"observed_at":"2026-08-06T12:47:05.083415Z","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":"2026-08-06T12:47:13.634679Z","title":"Formal verification of piece-wise linear feed-forward neural networks","venue":null,"work_id":"1e518f45-432b-4187-8886-ad489cabfcda","year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.173016Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:e596898f36091cb676ad6a6507154d1bb88c2a283f67f6842f1d9d8f84e6d036","observation_id":"312c2a71-b320-4db8-bfd9-df9854f53e26","resolution":{"observed_at":"2026-08-06T12:47:13.736843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:13.394634Z","title":"Strip: A defence against trojan attacks on deep neural networks","venue":null,"work_id":"9863e4bc-090e-4809-bad0-a664b9e028bc","year":2019},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.242795Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:6b8918ae568d8d1bc17b655f2e3adfcc3b3e78bbc3c4250af50015b56a1b358c","observation_id":"e88d2846-4ba1-4623-89c2-902a2576bef5","resolution":{"observed_at":"2026-08-06T12:47:13.539367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:13.087151Z","title":"Ai2: Safety and robustness certification of neural networks with abstract inter- pretation","venue":null,"work_id":"7931c812-130c-4933-8f9d-f77ef68afdd9","year":2018},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.302573Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:212b3cfc419131853172ff96f8a8a78a8b5c4843b996030a6bba023b8504ea37","observation_id":"8a5eaf86-7c58-42c7-9da5-6d9228c56626","resolution":{"observed_at":"2026-08-06T12:47:13.235951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-12T17:13:46.394331Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-06T12:47:05.394682Z","title":"Explaining and harnessing adversarial examples","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.394682Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:9944a578b933a4c7421ffdbdc4140d0e79466d89d11f016c60878c5508fd38c3","observation_id":"4db5ee45-013c-418f-a381-093ac71b9182","resolution":{"observed_at":"2026-08-06T12:47:05.394682Z","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":"2026-08-06T12:47:12.814340Z","title":"Badnets: Evaluating back- dooring attacks on deep neural networks","venue":null,"work_id":"a746f990-5e93-493e-8607-ae8c8dabcba4","year":2019},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.463171Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:fa5523f17eb9ed307720d5b64de77f90e182a21ef89c556138403ab6230ce784","observation_id":"c0f9cff4-f492-4dcc-9f89-f737e43ffa82","resolution":{"observed_at":"2026-08-06T12:47:12.919523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:12.575620Z","title":null,"venue":null,"work_id":"ab052416-0682-4495-85cc-1be4ffa996db","year":2020},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.556537Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:f980658180b4fe67a06f516e93dc4bb4982e6d000fcc068f1044066e2a641c32","observation_id":"bec7e872-d041-4723-9d52-8209101cd799","resolution":{"observed_at":"2026-08-06T12:47:12.667634Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:05.655425Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.655425Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:8b16d3bd3b15edc44556963eeb5c605ab850bb481b3550a0c653d6505be27803","observation_id":"9ff4d59c-ab42-4f41-af7f-6d2b1c0769fd","resolution":{"observed_at":"2026-08-06T12:47:05.655425Z","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":"2026-08-06T12:47:12.402098Z","title":"Adversarial example defense: Ensembles of weak defenses are not strong","venue":null,"work_id":"6fbd2eec-2e23-49b5-ac82-7a933f79ec17","year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.722789Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:909cbf5eec9981590ddec45cb332461eae1e694aff3e1b59adb1c8de4dbabce9","observation_id":"f34eba88-c43f-4aa9-86e7-ef0b11a2a7a4","resolution":{"observed_at":"2026-08-06T12:47:12.474729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:12.223413Z","title":"An overview of text-independent speaker recognition: From features to supervectors","venue":null,"work_id":"389adc5b-ea21-495c-a752-5ad586daa921","year":2010},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.821635Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:a5e145fe7b9285d226109305b9294fa71b964e1ccb3417859d70a0f46dbcd367","observation_id":"89b49520-d5dd-4335-a021-1749c038c79a","resolution":{"observed_at":"2026-08-06T12:47:12.292776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:12.043701Z","title":"Physgan: Generating physical-world-resilient adversarial examples for autonomous driving","venue":null,"work_id":"0d1ed6f2-5b32-4e80-a260-6b108cf05fe9","year":2020},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.887495Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:7c286c6bd2225e3cea7dfa7dbbecfb5de7526a875b9e3b5d14aa578bfb5539b1","observation_id":"63032a66-06dd-4042-83c1-6f570293346f","resolution":{"observed_at":"2026-08-06T12:47:12.105303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:11.838889Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":"bdaef8a9-259f-4e80-b17d-c74a10bfe4f3","year":2009},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:05.983954Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:6a0237ed05996f69d38d5ac34454d0503b891d7770894fca26f4342e4cdfd427","observation_id":"2b88d707-6e98-4d03-a6cf-24d43763ee87","resolution":{"observed_at":"2026-08-06T12:47:11.952604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:11.696244Z","title":"Adversarial examples in the physical world","venue":null,"work_id":"aae72098-1ec9-4156-91ce-d97e8f713c6b","year":2018},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.048026Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:080c07dd6527ca7ff5f97d41859888831b23e2c9093791d0a2e043bcdbccff90","observation_id":"56a80651-a1d4-4e7b-856e-526d64f4ed7b","resolution":{"observed_at":"2026-08-06T12:47:11.770267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:11.537006Z","title":"The mnist database of handwritten digits","venue":null,"work_id":"b3380922-d47f-480f-8aeb-6163df6fdc07","year":1998},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.147067Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:1a89b376ffee6f3bbd9cd40bc0edeca0f96f01fb547af760745c80762c0fd5d0","observation_id":"778fe0f7-c7be-458c-b43e-a9b67b272b52","resolution":{"observed_at":"2026-08-06T12:47:11.597553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.06083","last_updated":"2019-09-04T18:53:10Z","snapshot_observed_at":"2026-08-07T14:27:46.872660Z","submitted_at":"2017-06-19T17:53:11Z","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.06083","snapshot_observed_at":"2026-08-06T12:47:06.242168Z","title":"Towards deep learning models resistant to adversarial attacks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.242168Z"},"links":{"cited_paper":"/paper/1706.06083","citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:a718dfb23550f681b9d9b875531f3f523787f8eb5335151886f99702843b48ec","observation_id":"7ea0ce0d-65d5-43ad-ba28-29c0a8d801c0","resolution":{"observed_at":"2026-08-06T12:47:06.242168Z","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":"2026-08-06T12:47:11.332489Z","title":"Distributed representa- tions of words and phrases and their compositionality","venue":null,"work_id":"6abc8be2-ed3e-4a6b-8514-ac716f3c267b","year":2013},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.371920Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:999bd218c25e611c4ecf763663e604e6cfd79a736a69dea72fd311c7b6abbc5d","observation_id":"d83b942a-23e2-43cb-a19c-a6bb62ee76dc","resolution":{"observed_at":"2026-08-06T12:47:11.425501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:11.156398Z","title":"Universal adversarial perturbations","venue":null,"work_id":"0cbe44c7-13a4-4e9b-8502-62f0b0717cbd","year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.469548Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:2cfe6966d97ab063cc9b54b844d43074caec02f37cdbf6f6aee3780495b6824d","observation_id":"b7bbb448-2d5b-494d-84b0-c500a4a9feab","resolution":{"observed_at":"2026-08-06T12:47:11.239613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:06.562144Z","title":"Librispeech: an asr corpus based on public domain audio books","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.562144Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:169e5543ae83f6dba3b6944c27fdbcb203d733c461890f023b5e4f66dc1aa5e0","observation_id":"0af56ed1-41c3-46bf-978d-c34ebfa1035c","resolution":{"observed_at":"2026-08-06T12:47:06.562144Z","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":"2026-08-06T12:47:10.990152Z","title":"Distillation as a defense to adversarial perturbations against deep neural networks","venue":null,"work_id":"f434e6b0-0804-4f58-b448-24b419dd15d0","year":2016},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.670356Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:a780c8e49b3cb803a5750525f6f8831fd22e37f5a62bfbdea27051e164a26a2e","observation_id":"a332b05b-0681-4575-84ef-4d09a6a76820","resolution":{"observed_at":"2026-08-06T12:47:11.065412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:10.833609Z","title":"Deepxplore: Automated whitebox testing of deep learning systems","venue":null,"work_id":"10bcde89-a7ad-43ae-95d0-eb928567c44e","year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.752936Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:00dc84c9427134e04be2cc6a969b40540bd25963021bc5c1fcb242b8240c6098","observation_id":"70b7546f-827b-45a0-a4d7-3d6d45f00c0a","resolution":{"observed_at":"2026-08-06T12:47:10.904678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.04131","last_updated":"2018-03-20T10:10:10Z","snapshot_observed_at":"2026-08-14T20:47:52.040006Z","submitted_at":"2017-07-13T13:59:15Z","title":"Foolbox: A Python toolbox to benchmark the robustness of machine learning models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.04131","snapshot_observed_at":"2026-08-06T12:47:06.861003Z","title":"Foolbox: A python toolbox to benchmark the robustness of machine learning models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.861003Z"},"links":{"cited_paper":"/paper/1707.04131","citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:0dfd1a0d13d611038779f0f427e70fa71115219c736daf8b42951f5b5c455c55","observation_id":"c2f0d437-1757-46c8-9b0a-215fc11e5dc1","resolution":{"observed_at":"2026-08-06T12:47:06.861003Z","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":"2026-08-06T12:47:10.648543Z","title":"Deep learning in medical image analysis","venue":null,"work_id":"b83c6191-8769-4e81-bc42-3f4a9c1ab5bc","year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:06.937281Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:301cce77d910b87dfdf94fd61e627a3955c64a6631446d33533991047013b6a1","observation_id":"ad991c1f-5ae5-4efe-af55-f50d54e74607","resolution":{"observed_at":"2026-08-06T12:47:10.729261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:10.329137Z","title":"An abstract domain for certifying neural networks","venue":null,"work_id":"50e4294b-b68c-43f8-adce-9d778a9e6395","year":2019},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.044385Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:2336038a61edc2b013104a7e4f6b712f74d1b50138a86e36b2e22cb7aff23463","observation_id":"285f2424-e605-4d80-b18a-c0383ed78527","resolution":{"observed_at":"2026-08-06T12:47:10.492861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:10.038139Z","title":null,"venue":null,"work_id":"b867f1c4-c4b5-4a40-b3ca-1e0c20aa39bc","year":2012},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.144631Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:13f36448e0606eaedb418d30639afbb5b2461fd8429fa500932979028c40a917","observation_id":"d6dfb06d-7b95-4cc3-b486-40a7df2d41d8","resolution":{"observed_at":"2026-08-06T12:47:10.162154Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-08-15T16:41:15.505782Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-06T12:47:07.222769Z","title":"Intriguing properties of neural networks","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.222769Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:75dfddef16bc2181ae7d8f6db9c2f8e409a9715e32e921d999f4fc43fee0ee98","observation_id":"9d49d0b2-9b10-48df-998f-ee8f31a36c7e","resolution":{"observed_at":"2026-08-06T12:47:07.222769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.07356","last_updated":"2019-02-18T04:39:10Z","snapshot_observed_at":"2026-08-14T20:11:53.201463Z","submitted_at":"2017-11-20T15:05:33Z","title":"Evaluating Robustness of Neural Networks with Mixed Integer Programming","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.07356","snapshot_observed_at":"2026-08-06T12:47:07.329494Z","title":"Evaluating robustness of neural networks with mixed integer programming","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.329494Z"},"links":{"cited_paper":"/paper/1711.07356","citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:99643c8f9f95e2e3492c74533e93d796d513525e9b7966a1c0591aafd178ce81","observation_id":"3f38775c-42b0-4b13-897d-9834346cfda6","resolution":{"observed_at":"2026-08-06T12:47:07.329494Z","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":"2026-08-06T12:47:09.724878Z","title":"Dissector: Input validation for deep learning applications by crossing-layer dissection","venue":null,"work_id":"6058bae9-d5cc-4fba-a8bd-61c3b0cb7df3","year":2020},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.422428Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:16c136a8b5a1374103b4c8981c7f4e45d810277ddb245ba145d2af9ec1b6f36a","observation_id":"34aa2291-47e9-4319-a2d4-23e5bd1ac0b4","resolution":{"observed_at":"2026-08-06T12:47:09.862423Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:09.380295Z","title":"Adversarial sample detection for deep neural network through model mutation testing","venue":null,"work_id":"55aad528-df75-4e44-98e5-6f72da1c31c3","year":2019},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.510308Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:ceb1cf6ece8bcedef5c01cf3ac60b46fa8b4965f5d5154ef829de3c8fcb4f980","observation_id":"5e706821-6d49-4fda-b6d3-e8ce90fe3597","resolution":{"observed_at":"2026-08-06T12:47:09.544049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:09.143964Z","title":"Towards fast computation of certified robustness for relu networks","venue":null,"work_id":"aa137aeb-884e-4e28-a660-24e648087d22","year":2018},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.617362Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:1d531c31cffbbc3bd1b309eb9c904de6242c4fe51b4cd0a0e64b5544ae5046ea","observation_id":"c2770c1c-a5f6-4e11-9ccf-ce6106c2e0e6","resolution":{"observed_at":"2026-08-06T12:47:09.232633Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:08.883822Z","title":"Evaluating the robustness of neural networks: An extreme value theory approach","venue":null,"work_id":"33ae47af-4aaf-4326-b907-b6d1f75dba96","year":2018},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.733118Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:daf72085915710d66eacb38cac792603c865e80fc41e01d3ebe63dba30af83d2","observation_id":"044fd525-9959-4665-8225-eff7fff5288d","resolution":{"observed_at":"2026-08-06T12:47:08.996450Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:08.660681Z","title":"Adversarial sample detection for speaker verification by neural vocoders","venue":null,"work_id":"a8e216a1-273a-4695-b0ff-55f94fd41e47","year":2022},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.848985Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:5bc1b07b6223db1c5e6f1980655312bae61b1a3d62d7458c10845fb18ab3e690","observation_id":"e34e3104-4d91-4c4c-a99e-743d96158051","resolution":{"observed_at":"2026-08-06T12:47:08.749347Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:08.449132Z","title":"Droid-sec: deep learning in android malware detection","venue":null,"work_id":"30ac4e9c-93a8-4908-bdfe-fe2e82045fc9","year":2014},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:07.936495Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:327f7c94b18e609341828abb060daa894e43440d17c4fcff26a00f94993c0455","observation_id":"f78ac0fb-6dcd-4b12-8713-1b2e9caf5fe1","resolution":{"observed_at":"2026-08-06T12:47:08.537365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T12:47:08.270289Z","title":"Attack as defense: Characterizing adversarial examples using robustness","venue":null,"work_id":"294f770e-6cde-419f-b12f-2644cc141b53","year":2021},"citing_paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T12:47:08.051733Z"},"links":{"citing_paper":"/paper/2507.21483"},"observation_digest":"sha256:61dc1314655cd50cc43f3c7644efee9e4bd27d598a72b9b0d343fd4cedfc851a","observation_id":"35fec0fa-50b2-4889-929e-95493d4a05d9","resolution":{"observed_at":"2026-08-06T12:47:08.365228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.21483","last_updated":"2025-08-06T14:54:25Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-09T08:20:04.974576Z","submitted_at":"2025-07-29T03:58:20Z","title":"NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":27},"total_outbound_references":39},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.21483."}