{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:46GXBK2ZSYVHXQXRXL6VGA2PV6","short_pith_number":"pith:46GXBK2Z","canonical_record":{"source":{"id":"2101.12016","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CR","submitted_at":"2021-01-22T23:10:31Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"57b05bfee984ca32b9ca64c8d6d1d75ae0243baa6b49823cf0b8a40717c66b36","abstract_canon_sha256":"e19c737783516d83b4e81902ae04860ca58ddd683594c59ca9911e5344e2e7bd"},"schema_version":"1.0"},"canonical_sha256":"e78d70ab59962a7bc2f1bafd53034fafb596d4a6cf90ef2f4dde76317b4d570f","source":{"kind":"arxiv","id":"2101.12016","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.12016","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"arxiv_version","alias_value":"2101.12016v2","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.12016","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"pith_short_12","alias_value":"46GXBK2ZSYVH","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"pith_short_16","alias_value":"46GXBK2ZSYVHXQXR","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"pith_short_8","alias_value":"46GXBK2Z","created_at":"2026-07-05T02:14:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:46GXBK2ZSYVHXQXRXL6VGA2PV6","target":"record","payload":{"canonical_record":{"source":{"id":"2101.12016","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CR","submitted_at":"2021-01-22T23:10:31Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"57b05bfee984ca32b9ca64c8d6d1d75ae0243baa6b49823cf0b8a40717c66b36","abstract_canon_sha256":"e19c737783516d83b4e81902ae04860ca58ddd683594c59ca9911e5344e2e7bd"},"schema_version":"1.0"},"canonical_sha256":"e78d70ab59962a7bc2f1bafd53034fafb596d4a6cf90ef2f4dde76317b4d570f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:14:00.624655Z","signature_b64":"nDsO3JAHWFLgB1S5p+chOInOYl7RmaCzBU1za8gBgDtQjVPLu7gRuZL93j25APeA5rClX2QTpwktk2/wT8UbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e78d70ab59962a7bc2f1bafd53034fafb596d4a6cf90ef2f4dde76317b4d570f","last_reissued_at":"2026-07-05T02:14:00.624022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:14:00.624022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.12016","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:14:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DxuxJ7qCW/TEgsNtZxEar8UgP4BQ2h3LV8c3merQq9HMwfBbhUrq52xXhaNT97AZ/HQUzIt8HQYxfnNOb9iSCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T05:41:04.692837Z"},"content_sha256":"b39f2b81404e49cccd0c1195b0121c85128ecf628528abda13f867af87d5a951","schema_version":"1.0","event_id":"sha256:b39f2b81404e49cccd0c1195b0121c85128ecf628528abda13f867af87d5a951"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:46GXBK2ZSYVHXQXRXL6VGA2PV6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Baseline Pruning-Based Approach to Trojan Detection in Neural Networks","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Michael Majurski, Peter Bajcsy","submitted_at":"2021-01-22T23:10:31Z","abstract_excerpt":"This paper addresses the problem of detecting trojans in neural networks (NNs) by analyzing systematically pruned NN models. Our pruning-based approach consists of three main steps. First, detect any deviations from the reference look-up tables of model file sizes and model graphs. Next, measure the accuracy of a set of systematically pruned NN models following multiple pruning schemas. Finally, classify a NN model as clean or poisoned by applying a mapping between accuracy measurements and NN model labels. This work outlines a theoretical and experimental framework for finding the optimal map"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.12016","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/2101.12016/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:14:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gIMaTShIpWDRZ99gwcGcc/218Izd1lt7P7BryC3VQ6JY5AfaLZ/3m79cYllShg8JULPME7gHsKE1q4K1H/zvBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T05:41:04.693475Z"},"content_sha256":"8ed2c7028726568ac832368074cf58628dad91938a798955948e776cba2c8e65","schema_version":"1.0","event_id":"sha256:8ed2c7028726568ac832368074cf58628dad91938a798955948e776cba2c8e65"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/46GXBK2ZSYVHXQXRXL6VGA2PV6/bundle.json","state_url":"https://pith.science/pith/46GXBK2ZSYVHXQXRXL6VGA2PV6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/46GXBK2ZSYVHXQXRXL6VGA2PV6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-18T05:41:04Z","links":{"resolver":"https://pith.science/pith/46GXBK2ZSYVHXQXRXL6VGA2PV6","bundle":"https://pith.science/pith/46GXBK2ZSYVHXQXRXL6VGA2PV6/bundle.json","state":"https://pith.science/pith/46GXBK2ZSYVHXQXRXL6VGA2PV6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/46GXBK2ZSYVHXQXRXL6VGA2PV6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:46GXBK2ZSYVHXQXRXL6VGA2PV6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e19c737783516d83b4e81902ae04860ca58ddd683594c59ca9911e5344e2e7bd","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CR","submitted_at":"2021-01-22T23:10:31Z","title_canon_sha256":"57b05bfee984ca32b9ca64c8d6d1d75ae0243baa6b49823cf0b8a40717c66b36"},"schema_version":"1.0","source":{"id":"2101.12016","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.12016","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"arxiv_version","alias_value":"2101.12016v2","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.12016","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"pith_short_12","alias_value":"46GXBK2ZSYVH","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"pith_short_16","alias_value":"46GXBK2ZSYVHXQXR","created_at":"2026-07-05T02:14:00Z"},{"alias_kind":"pith_short_8","alias_value":"46GXBK2Z","created_at":"2026-07-05T02:14:00Z"}],"graph_snapshots":[{"event_id":"sha256:8ed2c7028726568ac832368074cf58628dad91938a798955948e776cba2c8e65","target":"graph","created_at":"2026-07-05T02:14:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2101.12016/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper addresses the problem of detecting trojans in neural networks (NNs) by analyzing systematically pruned NN models. Our pruning-based approach consists of three main steps. First, detect any deviations from the reference look-up tables of model file sizes and model graphs. Next, measure the accuracy of a set of systematically pruned NN models following multiple pruning schemas. Finally, classify a NN model as clean or poisoned by applying a mapping between accuracy measurements and NN model labels. This work outlines a theoretical and experimental framework for finding the optimal map","authors_text":"Michael Majurski, Peter Bajcsy","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CR","submitted_at":"2021-01-22T23:10:31Z","title":"Baseline Pruning-Based Approach to Trojan Detection in Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.12016","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b39f2b81404e49cccd0c1195b0121c85128ecf628528abda13f867af87d5a951","target":"record","created_at":"2026-07-05T02:14:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e19c737783516d83b4e81902ae04860ca58ddd683594c59ca9911e5344e2e7bd","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CR","submitted_at":"2021-01-22T23:10:31Z","title_canon_sha256":"57b05bfee984ca32b9ca64c8d6d1d75ae0243baa6b49823cf0b8a40717c66b36"},"schema_version":"1.0","source":{"id":"2101.12016","kind":"arxiv","version":2}},"canonical_sha256":"e78d70ab59962a7bc2f1bafd53034fafb596d4a6cf90ef2f4dde76317b4d570f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e78d70ab59962a7bc2f1bafd53034fafb596d4a6cf90ef2f4dde76317b4d570f","first_computed_at":"2026-07-05T02:14:00.624022Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:14:00.624022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nDsO3JAHWFLgB1S5p+chOInOYl7RmaCzBU1za8gBgDtQjVPLu7gRuZL93j25APeA5rClX2QTpwktk2/wT8UbBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:14:00.624655Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.12016","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b39f2b81404e49cccd0c1195b0121c85128ecf628528abda13f867af87d5a951","sha256:8ed2c7028726568ac832368074cf58628dad91938a798955948e776cba2c8e65"],"state_sha256":"89b42a6d5ee59535a0b4ec6baeee217485b4c41c3f2fda053ea84487fecb3d2f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v9ZrY5qokvZRbXH1yuz9X9pTDQ8Im2wZh7bLaPM0l9xB8pHWYT+mhyIpJqHmRlygnq1Vf/Vq8i3p9r5PfqmgDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T05:41:04.699143Z","bundle_sha256":"18f7bfa34eb478a0366e5f21c8288249a6bed8118637ef04b163cffb4bd4af26"}}