{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:BXNT6TNNF4E3WQKWAG4DOF4Q34","short_pith_number":"pith:BXNT6TNN","schema_version":"1.0","canonical_sha256":"0ddb3f4dad2f09bb415601b8371790df06be630e262cf45e247eb83068ae4972","source":{"kind":"arxiv","id":"1610.04256","version":1},"attestation_state":"computed","paper":{"title":"Assessing Threat of Adversarial Examples on Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abigail Graese, Andras Rozsa, Terrance E. Boult","submitted_at":"2016-10-13T20:34:48Z","abstract_excerpt":"Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For example, the proposed threat could result in hand-written digits on a scanned check being incorrectly classified but looking normal when humans see them. This research assesses the extent to which adversarial examples pose a security threat, when one considers the normal image acquisition process. This process is mimicked by simulating the transformations that normally occur in acquiring the image in a real world ap"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1610.04256","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-13T20:34:48Z","cross_cats_sorted":[],"title_canon_sha256":"a807938e4777a0a3170277635faed345c83bd78d6a2e0713b3e513fbb0de4016","abstract_canon_sha256":"a6cfd6a4026a58f262ec4507a5897ab89d6e1a7202373e69cce0cdfd93952fcc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:02:20.227258Z","signature_b64":"3WSLWUuTNySeK6Nyp/Kr0kpzBh4bQ0FNN59Iqzxc4KWAmu9mcgE9bye1F2CF+UQS9ITvFwHCgydOfxVCWoGuBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ddb3f4dad2f09bb415601b8371790df06be630e262cf45e247eb83068ae4972","last_reissued_at":"2026-05-18T01:02:20.226633Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:02:20.226633Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Assessing Threat of Adversarial Examples on Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abigail Graese, Andras Rozsa, Terrance E. Boult","submitted_at":"2016-10-13T20:34:48Z","abstract_excerpt":"Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For example, the proposed threat could result in hand-written digits on a scanned check being incorrectly classified but looking normal when humans see them. This research assesses the extent to which adversarial examples pose a security threat, when one considers the normal image acquisition process. This process is mimicked by simulating the transformations that normally occur in acquiring the image in a real world ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.04256","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1610.04256","created_at":"2026-05-18T01:02:20.226726+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.04256v1","created_at":"2026-05-18T01:02:20.226726+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.04256","created_at":"2026-05-18T01:02:20.226726+00:00"},{"alias_kind":"pith_short_12","alias_value":"BXNT6TNNF4E3","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"BXNT6TNNF4E3WQKW","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"BXNT6TNN","created_at":"2026-05-18T12:30:09.641336+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34","json":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34.json","graph_json":"https://pith.science/api/pith-number/BXNT6TNNF4E3WQKWAG4DOF4Q34/graph.json","events_json":"https://pith.science/api/pith-number/BXNT6TNNF4E3WQKWAG4DOF4Q34/events.json","paper":"https://pith.science/paper/BXNT6TNN"},"agent_actions":{"view_html":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34","download_json":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34.json","view_paper":"https://pith.science/paper/BXNT6TNN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.04256&json=true","fetch_graph":"https://pith.science/api/pith-number/BXNT6TNNF4E3WQKWAG4DOF4Q34/graph.json","fetch_events":"https://pith.science/api/pith-number/BXNT6TNNF4E3WQKWAG4DOF4Q34/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34/action/storage_attestation","attest_author":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34/action/author_attestation","sign_citation":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34/action/citation_signature","submit_replication":"https://pith.science/pith/BXNT6TNNF4E3WQKWAG4DOF4Q34/action/replication_record"}},"created_at":"2026-05-18T01:02:20.226726+00:00","updated_at":"2026-05-18T01:02:20.226726+00:00"}