{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:23KKRHRRP5OVWB3OQ34DF6U662","short_pith_number":"pith:23KKRHRR","schema_version":"1.0","canonical_sha256":"d6d4a89e317f5d5b076e86f832fa9ef6afe8b7e0b58a53a664a757b516131003","source":{"kind":"arxiv","id":"1802.07770","version":3},"attestation_state":"computed","paper":{"title":"Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Isabela Albuquerque, Jo\\~ao Monteiro, Tiago H. Falk, Zahid Akhtar","submitted_at":"2018-02-21T19:43:08Z","abstract_excerpt":"Modern applications of artificial neural networks have yielded remarkable performance gains in a wide range of tasks. However, recent studies have discovered that such modelling strategy is vulnerable to Adversarial Examples, i.e. examples with subtle perturbations often too small and imperceptible to humans, but that can easily fool neural networks. Defense techniques against adversarial examples have been proposed, but ensuring robust performance against varying or novel types of attacks remains an open problem. In this work, we focus on the detection setting, in which case attackers become "},"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":"1802.07770","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-21T19:43:08Z","cross_cats_sorted":[],"title_canon_sha256":"5fcffb7187688053f213fcbafe097c61b62bcf1f3bf30ab40961599eef938bc0","abstract_canon_sha256":"ff425087bd19696e0305c4653023e6661e6699278aa5f5a2ac17346ea7179c7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:02.044391Z","signature_b64":"UwIOBHASw7JWZxRwbA2sTLbQHgqeQ2Ipm9BeznF8BBkDaYA/PET6caFUqwb9S2ugFJpFS8eEu21pcIgAIOQMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6d4a89e317f5d5b076e86f832fa9ef6afe8b7e0b58a53a664a757b516131003","last_reissued_at":"2026-05-17T23:48:02.043693Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:02.043693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Isabela Albuquerque, Jo\\~ao Monteiro, Tiago H. Falk, Zahid Akhtar","submitted_at":"2018-02-21T19:43:08Z","abstract_excerpt":"Modern applications of artificial neural networks have yielded remarkable performance gains in a wide range of tasks. However, recent studies have discovered that such modelling strategy is vulnerable to Adversarial Examples, i.e. examples with subtle perturbations often too small and imperceptible to humans, but that can easily fool neural networks. Defense techniques against adversarial examples have been proposed, but ensuring robust performance against varying or novel types of attacks remains an open problem. In this work, we focus on the detection setting, in which case attackers become "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.07770","kind":"arxiv","version":3},"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":"1802.07770","created_at":"2026-05-17T23:48:02.043797+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.07770v3","created_at":"2026-05-17T23:48:02.043797+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.07770","created_at":"2026-05-17T23:48:02.043797+00:00"},{"alias_kind":"pith_short_12","alias_value":"23KKRHRRP5OV","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"23KKRHRRP5OVWB3O","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"23KKRHRR","created_at":"2026-05-18T12:31:59.375834+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/23KKRHRRP5OVWB3OQ34DF6U662","json":"https://pith.science/pith/23KKRHRRP5OVWB3OQ34DF6U662.json","graph_json":"https://pith.science/api/pith-number/23KKRHRRP5OVWB3OQ34DF6U662/graph.json","events_json":"https://pith.science/api/pith-number/23KKRHRRP5OVWB3OQ34DF6U662/events.json","paper":"https://pith.science/paper/23KKRHRR"},"agent_actions":{"view_html":"https://pith.science/pith/23KKRHRRP5OVWB3OQ34DF6U662","download_json":"https://pith.science/pith/23KKRHRRP5OVWB3OQ34DF6U662.json","view_paper":"https://pith.science/paper/23KKRHRR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.07770&json=true","fetch_graph":"https://pith.science/api/pith-number/23KKRHRRP5OVWB3OQ34DF6U662/graph.json","fetch_events":"https://pith.science/api/pith-number/23KKRHRRP5OVWB3OQ34DF6U662/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/23KKRHRRP5OVWB3OQ34DF6U662/action/timestamp_anchor","attest_storage":"https://pith.science/pith/23KKRHRRP5OVWB3OQ34DF6U662/action/storage_attestation","attest_author":"https://pith.science/pith/23KKRHRRP5OVWB3OQ34DF6U662/action/author_attestation","sign_citation":"https://pith.science/pith/23KKRHRRP5OVWB3OQ34DF6U662/action/citation_signature","submit_replication":"https://pith.science/pith/23KKRHRRP5OVWB3OQ34DF6U662/action/replication_record"}},"created_at":"2026-05-17T23:48:02.043797+00:00","updated_at":"2026-05-17T23:48:02.043797+00:00"}