{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:OSLQ7SVNRMNCL6E7I47F3W7LV5","short_pith_number":"pith:OSLQ7SVN","schema_version":"1.0","canonical_sha256":"74970fcaad8b1a25f89f473e5ddbebaf4d0169d349dd9a45ff717f65fe96f358","source":{"kind":"arxiv","id":"2103.15918","version":2},"attestation_state":"computed","paper":{"title":"MISA: Online Defense of Trojaned Models using Misattributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.CR","authors_text":"Anirban Roy, Karan Sikka, Panagiota Kiourti, Susmit Jha, Wenchao Li","submitted_at":"2021-03-29T19:53:44Z","abstract_excerpt":"Recent studies have shown that neural networks are vulnerable to Trojan attacks, where a network is trained to respond to specially crafted trigger patterns in the inputs in specific and potentially malicious ways. This paper proposes MISA, a new online approach to detect Trojan triggers for neural networks at inference time. Our approach is based on a novel notion called misattributions, which captures the anomalous manifestation of a Trojan activation in the feature space. Given an input image and the corresponding output prediction, our algorithm first computes the model's attribution on di"},"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":"2103.15918","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2021-03-29T19:53:44Z","cross_cats_sorted":["cs.CV","stat.ML"],"title_canon_sha256":"8e01cf4c32960a1bf98aac269738b81e9d44f3091b3993a8cba728f4d6028c92","abstract_canon_sha256":"3510d1bd218f6d371f940f8f5bc1e5042400a7165ccf30b5f0db1affcf7778ff"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:17:02.371995Z","signature_b64":"0h3cfu4kBKd4GlnY7RJzX9/fBuPtHGhAj42WfmXIf+Ct3gFkQQK/sTPQgeyAGuKhWAm/dS+qcJcqmhtiqeu/Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74970fcaad8b1a25f89f473e5ddbebaf4d0169d349dd9a45ff717f65fe96f358","last_reissued_at":"2026-07-05T03:17:02.371652Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:17:02.371652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MISA: Online Defense of Trojaned Models using Misattributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.ML"],"primary_cat":"cs.CR","authors_text":"Anirban Roy, Karan Sikka, Panagiota Kiourti, Susmit Jha, Wenchao Li","submitted_at":"2021-03-29T19:53:44Z","abstract_excerpt":"Recent studies have shown that neural networks are vulnerable to Trojan attacks, where a network is trained to respond to specially crafted trigger patterns in the inputs in specific and potentially malicious ways. This paper proposes MISA, a new online approach to detect Trojan triggers for neural networks at inference time. Our approach is based on a novel notion called misattributions, which captures the anomalous manifestation of a Trojan activation in the feature space. Given an input image and the corresponding output prediction, our algorithm first computes the model's attribution on di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.15918","kind":"arxiv","version":2},"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/2103.15918/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":"2103.15918","created_at":"2026-07-05T03:17:02.371707+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.15918v2","created_at":"2026-07-05T03:17:02.371707+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.15918","created_at":"2026-07-05T03:17:02.371707+00:00"},{"alias_kind":"pith_short_12","alias_value":"OSLQ7SVNRMNC","created_at":"2026-07-05T03:17:02.371707+00:00"},{"alias_kind":"pith_short_16","alias_value":"OSLQ7SVNRMNCL6E7","created_at":"2026-07-05T03:17:02.371707+00:00"},{"alias_kind":"pith_short_8","alias_value":"OSLQ7SVN","created_at":"2026-07-05T03:17:02.371707+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/OSLQ7SVNRMNCL6E7I47F3W7LV5","json":"https://pith.science/pith/OSLQ7SVNRMNCL6E7I47F3W7LV5.json","graph_json":"https://pith.science/api/pith-number/OSLQ7SVNRMNCL6E7I47F3W7LV5/graph.json","events_json":"https://pith.science/api/pith-number/OSLQ7SVNRMNCL6E7I47F3W7LV5/events.json","paper":"https://pith.science/paper/OSLQ7SVN"},"agent_actions":{"view_html":"https://pith.science/pith/OSLQ7SVNRMNCL6E7I47F3W7LV5","download_json":"https://pith.science/pith/OSLQ7SVNRMNCL6E7I47F3W7LV5.json","view_paper":"https://pith.science/paper/OSLQ7SVN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.15918&json=true","fetch_graph":"https://pith.science/api/pith-number/OSLQ7SVNRMNCL6E7I47F3W7LV5/graph.json","fetch_events":"https://pith.science/api/pith-number/OSLQ7SVNRMNCL6E7I47F3W7LV5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OSLQ7SVNRMNCL6E7I47F3W7LV5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OSLQ7SVNRMNCL6E7I47F3W7LV5/action/storage_attestation","attest_author":"https://pith.science/pith/OSLQ7SVNRMNCL6E7I47F3W7LV5/action/author_attestation","sign_citation":"https://pith.science/pith/OSLQ7SVNRMNCL6E7I47F3W7LV5/action/citation_signature","submit_replication":"https://pith.science/pith/OSLQ7SVNRMNCL6E7I47F3W7LV5/action/replication_record"}},"created_at":"2026-07-05T03:17:02.371707+00:00","updated_at":"2026-07-05T03:17:02.371707+00:00"}