{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:76QBISRY6SRHD4ZXMHY7OLMQ5V","short_pith_number":"pith:76QBISRY","schema_version":"1.0","canonical_sha256":"ffa0144a38f4a271f33761f1f72d90ed5be59ae119c2a575e8364084ea11349d","source":{"kind":"arxiv","id":"2202.07183","version":1},"attestation_state":"computed","paper":{"title":"A Survey of Neural Trojan Attacks and Defenses in Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.CR","authors_text":"Ghulam Mubashar Hassan, Jie Wang, Naveed Akhtar","submitted_at":"2022-02-15T04:26:44Z","abstract_excerpt":"Artificial Intelligence (AI) relies heavily on deep learning - a technology that is becoming increasingly popular in real-life applications of AI, even in the safety-critical and high-risk domains. However, it is recently discovered that deep learning can be manipulated by embedding Trojans inside it. Unfortunately, pragmatic solutions to circumvent the computational requirements of deep learning, e.g. outsourcing model training or data annotation to third parties, further add to model susceptibility to the Trojan attacks. Due to the key importance of the topic in deep learning, recent literat"},"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":"2202.07183","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2022-02-15T04:26:44Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"f344c7f29a11356c66971434a77a9fa0afa81a23fc269839a82ee8a51dd88577","abstract_canon_sha256":"5f7d6eae3cab3a0eee4c154441860b6a40fc6204003c3e37cb1a5289e6218832"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:56:57.397535Z","signature_b64":"mKMu4n9biisO2V/5vOMJBgnj/LlHp9i0//mg6oTSMKVGzNuPRmSYVJ8GIWnLXNuz8V2D5p8pgw9181MvzbPCBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffa0144a38f4a271f33761f1f72d90ed5be59ae119c2a575e8364084ea11349d","last_reissued_at":"2026-07-05T03:56:57.396898Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:56:57.396898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey of Neural Trojan Attacks and Defenses in Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.CR","authors_text":"Ghulam Mubashar Hassan, Jie Wang, Naveed Akhtar","submitted_at":"2022-02-15T04:26:44Z","abstract_excerpt":"Artificial Intelligence (AI) relies heavily on deep learning - a technology that is becoming increasingly popular in real-life applications of AI, even in the safety-critical and high-risk domains. However, it is recently discovered that deep learning can be manipulated by embedding Trojans inside it. Unfortunately, pragmatic solutions to circumvent the computational requirements of deep learning, e.g. outsourcing model training or data annotation to third parties, further add to model susceptibility to the Trojan attacks. Due to the key importance of the topic in deep learning, recent literat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.07183","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/2202.07183/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":"2202.07183","created_at":"2026-07-05T03:56:57.396962+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.07183v1","created_at":"2026-07-05T03:56:57.396962+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.07183","created_at":"2026-07-05T03:56:57.396962+00:00"},{"alias_kind":"pith_short_12","alias_value":"76QBISRY6SRH","created_at":"2026-07-05T03:56:57.396962+00:00"},{"alias_kind":"pith_short_16","alias_value":"76QBISRY6SRHD4ZX","created_at":"2026-07-05T03:56:57.396962+00:00"},{"alias_kind":"pith_short_8","alias_value":"76QBISRY","created_at":"2026-07-05T03:56:57.396962+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/76QBISRY6SRHD4ZXMHY7OLMQ5V","json":"https://pith.science/pith/76QBISRY6SRHD4ZXMHY7OLMQ5V.json","graph_json":"https://pith.science/api/pith-number/76QBISRY6SRHD4ZXMHY7OLMQ5V/graph.json","events_json":"https://pith.science/api/pith-number/76QBISRY6SRHD4ZXMHY7OLMQ5V/events.json","paper":"https://pith.science/paper/76QBISRY"},"agent_actions":{"view_html":"https://pith.science/pith/76QBISRY6SRHD4ZXMHY7OLMQ5V","download_json":"https://pith.science/pith/76QBISRY6SRHD4ZXMHY7OLMQ5V.json","view_paper":"https://pith.science/paper/76QBISRY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.07183&json=true","fetch_graph":"https://pith.science/api/pith-number/76QBISRY6SRHD4ZXMHY7OLMQ5V/graph.json","fetch_events":"https://pith.science/api/pith-number/76QBISRY6SRHD4ZXMHY7OLMQ5V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/76QBISRY6SRHD4ZXMHY7OLMQ5V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/76QBISRY6SRHD4ZXMHY7OLMQ5V/action/storage_attestation","attest_author":"https://pith.science/pith/76QBISRY6SRHD4ZXMHY7OLMQ5V/action/author_attestation","sign_citation":"https://pith.science/pith/76QBISRY6SRHD4ZXMHY7OLMQ5V/action/citation_signature","submit_replication":"https://pith.science/pith/76QBISRY6SRHD4ZXMHY7OLMQ5V/action/replication_record"}},"created_at":"2026-07-05T03:56:57.396962+00:00","updated_at":"2026-07-05T03:56:57.396962+00:00"}