{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:7JJWT4TNLGUW4FVCYQRBPHNJIU","short_pith_number":"pith:7JJWT4TN","schema_version":"1.0","canonical_sha256":"fa5369f26d59a96e16a2c422179da945257afc31b338853a83adceccc0e0e5d4","source":{"kind":"arxiv","id":"2111.09961","version":1},"attestation_state":"computed","paper":{"title":"A Review of Adversarial Attack and Defense for Classification Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Cho-Jui Hsieh, Minhao Cheng, Thomas C. M. Lee, Yao Li","submitted_at":"2021-11-18T22:13:43Z","abstract_excerpt":"Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many classification methods, especially deep neural networks (DNNs), are vulnerable to adversarial examples; i.e., examples that are carefully crafted to fool a well-trained classification model while being indistinguishable from natural data to human. This makes it potentially unsafe to apply DNNs or related methods in security-critical areas. Since this issue was first identified by Biggio et al. (2013) and Szegedy et al.(2014), much work has been done in this field, including the develo"},"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":"2111.09961","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2021-11-18T22:13:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"250f4a9e7300e684814d4d5455f7a008bae8a6bed8b5915cb70d3f20d3a0e48f","abstract_canon_sha256":"2f6c374866c3b302e2e6a3248af9940ab48a9251259e23f758dc40a011c7acbb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:33:26.252798Z","signature_b64":"hfIINzDsWL+tFMgmnDy4f+RlaGK/0drpejfawkVpD9rtnUKN0BeC+t4hoFUjAsVvk7pIjqPNvxX8tKB7CZtmBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa5369f26d59a96e16a2c422179da945257afc31b338853a83adceccc0e0e5d4","last_reissued_at":"2026-07-05T03:33:26.252359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:33:26.252359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Review of Adversarial Attack and Defense for Classification Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Cho-Jui Hsieh, Minhao Cheng, Thomas C. M. Lee, Yao Li","submitted_at":"2021-11-18T22:13:43Z","abstract_excerpt":"Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many classification methods, especially deep neural networks (DNNs), are vulnerable to adversarial examples; i.e., examples that are carefully crafted to fool a well-trained classification model while being indistinguishable from natural data to human. This makes it potentially unsafe to apply DNNs or related methods in security-critical areas. Since this issue was first identified by Biggio et al. (2013) and Szegedy et al.(2014), much work has been done in this field, including the develo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.09961","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.09961/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2111.09961","created_at":"2026-07-05T03:33:26.252425+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.09961v1","created_at":"2026-07-05T03:33:26.252425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.09961","created_at":"2026-07-05T03:33:26.252425+00:00"},{"alias_kind":"pith_short_12","alias_value":"7JJWT4TNLGUW","created_at":"2026-07-05T03:33:26.252425+00:00"},{"alias_kind":"pith_short_16","alias_value":"7JJWT4TNLGUW4FVC","created_at":"2026-07-05T03:33:26.252425+00:00"},{"alias_kind":"pith_short_8","alias_value":"7JJWT4TN","created_at":"2026-07-05T03:33:26.252425+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.14946","citing_title":"Reliable Evaluation of Attribution Maps in CNNs: A Perturbation-Based Approach","ref_index":47,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU","json":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU.json","graph_json":"https://pith.science/api/pith-number/7JJWT4TNLGUW4FVCYQRBPHNJIU/graph.json","events_json":"https://pith.science/api/pith-number/7JJWT4TNLGUW4FVCYQRBPHNJIU/events.json","paper":"https://pith.science/paper/7JJWT4TN"},"agent_actions":{"view_html":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU","download_json":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU.json","view_paper":"https://pith.science/paper/7JJWT4TN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.09961&json=true","fetch_graph":"https://pith.science/api/pith-number/7JJWT4TNLGUW4FVCYQRBPHNJIU/graph.json","fetch_events":"https://pith.science/api/pith-number/7JJWT4TNLGUW4FVCYQRBPHNJIU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU/action/storage_attestation","attest_author":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU/action/author_attestation","sign_citation":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU/action/citation_signature","submit_replication":"https://pith.science/pith/7JJWT4TNLGUW4FVCYQRBPHNJIU/action/replication_record"}},"created_at":"2026-07-05T03:33:26.252425+00:00","updated_at":"2026-07-05T03:33:26.252425+00:00"}