{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:VYBHNH3267UAAG3TQETEQJHFZI","short_pith_number":"pith:VYBHNH32","schema_version":"1.0","canonical_sha256":"ae02769f7af7e8001b7381264824e5ca1494b8d4a46964e0ab937e105f7def70","source":{"kind":"arxiv","id":"1810.12576","version":1},"attestation_state":"computed","paper":{"title":"Improved Network Robustness with Adversary Critic","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Matyasko, Lap-Pui Chau","submitted_at":"2018-10-30T08:33:46Z","abstract_excerpt":"Ideally, what confuses neural network should be confusing to humans. However, recent experiments have shown that small, imperceptible perturbations can change the network prediction. To address this gap in perception, we propose a novel approach for learning robust classifier. Our main idea is: adversarial examples for the robust classifier should be indistinguishable from the regular data of the adversarial target. We formulate a problem of learning robust classifier in the framework of Generative Adversarial Networks (GAN), where the adversarial attack on classifier acts as a generator, and "},"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":"1810.12576","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-30T08:33:46Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"f0503483702f2b653e4644a2766dcc2333ea4c19c236e08a5c7964b0055c8a47","abstract_canon_sha256":"f9903e5a3dfa4a9078c82dab51ae9f3a23a3c23a3542ffef11cdafb4fa75f6e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:56.634444Z","signature_b64":"hZi16PeVRVotjy7L07/8GdJ3Hv4FhwWsUcYsJ9JfJR1OzHjZSYWMV15mlikifmYaYFg7Rn+yBzRD0Q8drncuAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae02769f7af7e8001b7381264824e5ca1494b8d4a46964e0ab937e105f7def70","last_reissued_at":"2026-05-18T00:01:56.633755Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:56.633755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved Network Robustness with Adversary Critic","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Matyasko, Lap-Pui Chau","submitted_at":"2018-10-30T08:33:46Z","abstract_excerpt":"Ideally, what confuses neural network should be confusing to humans. However, recent experiments have shown that small, imperceptible perturbations can change the network prediction. To address this gap in perception, we propose a novel approach for learning robust classifier. Our main idea is: adversarial examples for the robust classifier should be indistinguishable from the regular data of the adversarial target. We formulate a problem of learning robust classifier in the framework of Generative Adversarial Networks (GAN), where the adversarial attack on classifier acts as a generator, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.12576","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":"1810.12576","created_at":"2026-05-18T00:01:56.633862+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.12576v1","created_at":"2026-05-18T00:01:56.633862+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.12576","created_at":"2026-05-18T00:01:56.633862+00:00"},{"alias_kind":"pith_short_12","alias_value":"VYBHNH3267UA","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"VYBHNH3267UAAG3T","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"VYBHNH32","created_at":"2026-05-18T12:32:59.047623+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/VYBHNH3267UAAG3TQETEQJHFZI","json":"https://pith.science/pith/VYBHNH3267UAAG3TQETEQJHFZI.json","graph_json":"https://pith.science/api/pith-number/VYBHNH3267UAAG3TQETEQJHFZI/graph.json","events_json":"https://pith.science/api/pith-number/VYBHNH3267UAAG3TQETEQJHFZI/events.json","paper":"https://pith.science/paper/VYBHNH32"},"agent_actions":{"view_html":"https://pith.science/pith/VYBHNH3267UAAG3TQETEQJHFZI","download_json":"https://pith.science/pith/VYBHNH3267UAAG3TQETEQJHFZI.json","view_paper":"https://pith.science/paper/VYBHNH32","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.12576&json=true","fetch_graph":"https://pith.science/api/pith-number/VYBHNH3267UAAG3TQETEQJHFZI/graph.json","fetch_events":"https://pith.science/api/pith-number/VYBHNH3267UAAG3TQETEQJHFZI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VYBHNH3267UAAG3TQETEQJHFZI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VYBHNH3267UAAG3TQETEQJHFZI/action/storage_attestation","attest_author":"https://pith.science/pith/VYBHNH3267UAAG3TQETEQJHFZI/action/author_attestation","sign_citation":"https://pith.science/pith/VYBHNH3267UAAG3TQETEQJHFZI/action/citation_signature","submit_replication":"https://pith.science/pith/VYBHNH3267UAAG3TQETEQJHFZI/action/replication_record"}},"created_at":"2026-05-18T00:01:56.633862+00:00","updated_at":"2026-05-18T00:01:56.633862+00:00"}