{"as_of":"2026-08-08T03:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d54f9592300575060750c8ea209abee41534746a814be14be5500123dcbb70fa","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T20:29:43.143790Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-25T15:35:58.936154Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1710.08864","last_updated":"2019-10-17T07:46:53Z","snapshot_observed_at":"2026-08-03T20:06:38.460006Z","submitted_at":"2017-10-24T16:02:19Z","title":"One pixel attack for fooling deep neural networks","version":7},"cited_work":{"arxiv_id":"1710.08864","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1710.08864","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"CoRR abs/1710.08864 (2017), http://arxiv.org/abs/1710.08864","venue":null,"work_id":"71c2298c-c0e4-4457-8c21-1f5cce3ece54","year":2017},"citing_paper":{"arxiv_id":"1906.01820","last_updated":"2021-12-01T11:22:52Z","snapshot_observed_at":"2026-08-01T23:32:03.638122Z","submitted_at":"2019-06-05T04:43:25Z","title":"Risks from Learned Optimization in Advanced Machine Learning Systems","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-15T12:21:53.058760Z"},"links":{"cited_paper":"/paper/1710.08864","citing_paper":"/paper/1906.01820"},"observation_digest":"sha256:c3b03f536153222cafbe505c9fa5554462c14cd32854d237e53460f3bff4b39f","observation_id":"fa80d621-16dd-486f-a60a-65b00476ae76","resolution":{"observed_at":"2026-05-15T12:21:53.120569Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.08864","last_updated":"2019-10-17T07:46:53Z","snapshot_observed_at":"2026-08-03T20:06:38.460006Z","submitted_at":"2017-10-24T16:02:19Z","title":"One pixel attack for fooling deep neural networks","version":7},"cited_work":{"arxiv_id":"1710.08864","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1710.08864","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"CoRR abs/1710.08864 (2017), http://arxiv.org/abs/1710.08864","venue":null,"work_id":"71c2298c-c0e4-4457-8c21-1f5cce3ece54","year":2017},"citing_paper":{"arxiv_id":"1906.11328","last_updated":"2019-06-26T20:16:17Z","snapshot_observed_at":"2026-08-02T01:15:26.405072Z","submitted_at":"2019-06-26T20:16:17Z","title":"Adversarial FDI Attack against AC State Estimation with ANN","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-25T15:33:10.899090Z"},"links":{"cited_paper":"/paper/1710.08864","citing_paper":"/paper/1906.11328"},"observation_digest":"sha256:652699db2f4fabb057a40268730a19da8e88ece0e8b0a60acf932ffd15944660","observation_id":"1dcb1644-4a19-4109-8a46-cef114be6632","resolution":{"observed_at":"2026-05-25T15:35:58.940138Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.08864","last_updated":"2019-10-17T07:46:53Z","snapshot_observed_at":"2026-08-03T20:06:38.460006Z","submitted_at":"2017-10-24T16:02:19Z","title":"One pixel attack for fooling deep neural networks","version":7},"cited_work":{"arxiv_id":"1710.08864","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1710.08864","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"CoRR abs/1710.08864 (2017), http://arxiv.org/abs/1710.08864","venue":null,"work_id":"71c2298c-c0e4-4457-8c21-1f5cce3ece54","year":2017},"citing_paper":{"arxiv_id":"1907.06291","last_updated":"2019-07-14T22:20:58Z","snapshot_observed_at":"2026-08-05T22:23:54.509343Z","submitted_at":"2019-07-14T22:20:58Z","title":"Measuring the Transferability of Adversarial Examples","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T21:26:53.670675Z"},"links":{"cited_paper":"/paper/1710.08864","citing_paper":"/paper/1907.06291"},"observation_digest":"sha256:22e7f3c923d9fa9bdd33c5304c20bf8f5e2c1303094147add7696aad43822b6f","observation_id":"ed6e61e7-0b8c-4a98-8041-0a4028647375","resolution":{"observed_at":"2026-05-24T21:29:57.933911Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.08864","last_updated":"2019-10-17T07:46:53Z","snapshot_observed_at":"2026-08-03T20:06:38.460006Z","submitted_at":"2017-10-24T16:02:19Z","title":"One pixel attack for fooling deep neural networks","version":7},"cited_work":{"arxiv_id":"1710.08864","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1710.08864","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"CoRR abs/1710.08864 (2017), http://arxiv.org/abs/1710.08864","venue":null,"work_id":"71c2298c-c0e4-4457-8c21-1f5cce3ece54","year":2017},"citing_paper":{"arxiv_id":"1907.08965","last_updated":"2019-07-21T12:35:43Z","snapshot_observed_at":"2026-08-03T00:39:34.988429Z","submitted_at":"2019-07-21T12:35:43Z","title":"Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges","version":1},"reference_index":181,"source":"pdf_text","source_observed_at":"2026-05-24T18:22:23.272044Z"},"links":{"cited_paper":"/paper/1710.08864","citing_paper":"/paper/1907.08965"},"observation_digest":"sha256:1e0f755a548708b1c50df81f8867a1e2e5c2dd5c14bb4e78204f3fb213b38479","observation_id":"85e47f5a-f06f-4fca-b474-8df13e88bc73","resolution":{"observed_at":"2026-05-24T18:24:48.154343Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.08864","last_updated":"2019-10-17T07:46:53Z","snapshot_observed_at":"2026-08-03T20:06:38.460006Z","submitted_at":"2017-10-24T16:02:19Z","title":"One pixel attack for fooling deep neural networks","version":7},"cited_work":{"arxiv_id":"1710.08864","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1710.08864","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"CoRR abs/1710.08864 (2017), http://arxiv.org/abs/1710.08864","venue":null,"work_id":"71c2298c-c0e4-4457-8c21-1f5cce3ece54","year":2017},"citing_paper":{"arxiv_id":"1907.10406","last_updated":"2019-07-21T11:52:36Z","snapshot_observed_at":"2026-07-06T08:09:51.382237Z","submitted_at":"2019-07-21T11:52:36Z","title":"Open DNN Box by Power Side-Channel Attack","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-24T18:44:12.031882Z"},"links":{"cited_paper":"/paper/1710.08864","citing_paper":"/paper/1907.10406"},"observation_digest":"sha256:a39f3eeeb70643ce83673a81f78c034b98f17b3c4c0a51f04c2d72af229b9da6","observation_id":"c68b02cb-6335-4519-b6aa-38da5f5ee439","resolution":{"observed_at":"2026-05-24T18:44:49.273911Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.08864","last_updated":"2019-10-17T07:46:53Z","snapshot_observed_at":"2026-08-03T20:06:38.460006Z","submitted_at":"2017-10-24T16:02:19Z","title":"One pixel attack for fooling deep neural networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.08864","snapshot_observed_at":"2026-08-05T20:29:43.143790Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.10490","last_updated":"2025-08-14T09:49:07Z","snapshot_observed_at":"2026-08-07T18:25:37.859850Z","submitted_at":"2025-08-14T09:49:07Z","title":"On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T20:29:43.143790Z"},"links":{"cited_paper":"/paper/1710.08864","citing_paper":"/paper/2508.10490"},"observation_digest":"sha256:4f009420e6ceaea93ba7ca90862ebe3cde693da15649b661f8d657f2dd1a8296","observation_id":"97867edc-21c4-4b30-8291-87b78b19eb11","resolution":{"observed_at":"2026-08-05T20:29:43.143790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1710.08864/citation-record","integrity":"/paper/1710.08864/integrity","json":"/paper/1710.08864/citation-record.json","paper":"/paper/1710.08864"},"outbound":[],"paper":{"arxiv_id":"1710.08864","last_updated":"2019-10-17T07:46:53Z","latest_version":7,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T20:06:38.460006Z","submitted_at":"2017-10-24T16:02:19Z","title":"One pixel attack for fooling deep neural networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1710.08864."}