{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:IHHQD4TFTSWDAB32WIOJQRFABC","short_pith_number":"pith:IHHQD4TF","canonical_record":{"source":{"id":"2212.10006","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T05:35:15Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"e1cb941b191e6fa12aa76e667e79086be04663958313b25662f1dc94e57ac0fc","abstract_canon_sha256":"1d6be445e6651a8301655236155365d664704632680713503d5de2bcce3d022c"},"schema_version":"1.0"},"canonical_sha256":"41cf01f2659cac30077ab21c9844a008b9f8df14f0ce672b340c099e792ef5cb","source":{"kind":"arxiv","id":"2212.10006","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10006","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10006v1","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10006","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"pith_short_12","alias_value":"IHHQD4TFTSWD","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"pith_short_16","alias_value":"IHHQD4TFTSWDAB32","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"pith_short_8","alias_value":"IHHQD4TF","created_at":"2026-07-05T05:27:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:IHHQD4TFTSWDAB32WIOJQRFABC","target":"record","payload":{"canonical_record":{"source":{"id":"2212.10006","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T05:35:15Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"e1cb941b191e6fa12aa76e667e79086be04663958313b25662f1dc94e57ac0fc","abstract_canon_sha256":"1d6be445e6651a8301655236155365d664704632680713503d5de2bcce3d022c"},"schema_version":"1.0"},"canonical_sha256":"41cf01f2659cac30077ab21c9844a008b9f8df14f0ce672b340c099e792ef5cb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:27:02.669522Z","signature_b64":"X5Y8ucfHKdyZnAD4MZpkxTifH55I5gMldlgYGiSNhOSQp71nhLtfNgb9L7BpSNvjRwBiq0cx53WtH/FnO7TjDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41cf01f2659cac30077ab21c9844a008b9f8df14f0ce672b340c099e792ef5cb","last_reissued_at":"2026-07-05T05:27:02.669163Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:27:02.669163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.10006","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:27:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wimb4Q9AtnxKBdfaGDRm1MpcRafmJXHWDzhPEF076lXN/GaQr1ROD7gikR19POJ5x5GXgbvTqFcLUbwCFEBDBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:47:34.102897Z"},"content_sha256":"5c0465090f105e1df22328dbff96d79f94860c16d502b70cba88995695cb0551","schema_version":"1.0","event_id":"sha256:5c0465090f105e1df22328dbff96d79f94860c16d502b70cba88995695cb0551"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:IHHQD4TFTSWDAB32WIOJQRFABC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-head Uncertainty Inference for Adversarial Attack Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.LG","authors_text":"Jiyang Xie. Zhongwei Si, Kai Guo, Ke Zhang, Kongming Liang, Songyun Yang, Yuqi Yang","submitted_at":"2022-12-20T05:35:15Z","abstract_excerpt":"Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed in recent years to overcome the adversarial attacks. In this paper, we propose a multi-head uncertainty inference (MH-UI) framework for detecting adversarial attack examples. We adopt a multi-head architecture with multiple prediction heads (i.e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10006","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/2212.10006/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":"2026-07-05T05:27:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4PwhnExySYk9YABwPh7scaDa5wiQTrztQkjZFoPGWP/B7JQ3xbstJLvCrS9A/enLKZy9ZAOCp7lsyDAgpZeGBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T16:47:34.103914Z"},"content_sha256":"274b72f032d70290b47fa36609a6867a020e1449337a5d5cec9d6c9d8da059a4","schema_version":"1.0","event_id":"sha256:274b72f032d70290b47fa36609a6867a020e1449337a5d5cec9d6c9d8da059a4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IHHQD4TFTSWDAB32WIOJQRFABC/bundle.json","state_url":"https://pith.science/pith/IHHQD4TFTSWDAB32WIOJQRFABC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IHHQD4TFTSWDAB32WIOJQRFABC/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":"2026-08-07T16:47:34Z","links":{"resolver":"https://pith.science/pith/IHHQD4TFTSWDAB32WIOJQRFABC","bundle":"https://pith.science/pith/IHHQD4TFTSWDAB32WIOJQRFABC/bundle.json","state":"https://pith.science/pith/IHHQD4TFTSWDAB32WIOJQRFABC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IHHQD4TFTSWDAB32WIOJQRFABC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:IHHQD4TFTSWDAB32WIOJQRFABC","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":"1d6be445e6651a8301655236155365d664704632680713503d5de2bcce3d022c","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T05:35:15Z","title_canon_sha256":"e1cb941b191e6fa12aa76e667e79086be04663958313b25662f1dc94e57ac0fc"},"schema_version":"1.0","source":{"id":"2212.10006","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.10006","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"arxiv_version","alias_value":"2212.10006v1","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10006","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"pith_short_12","alias_value":"IHHQD4TFTSWD","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"pith_short_16","alias_value":"IHHQD4TFTSWDAB32","created_at":"2026-07-05T05:27:02Z"},{"alias_kind":"pith_short_8","alias_value":"IHHQD4TF","created_at":"2026-07-05T05:27:02Z"}],"graph_snapshots":[{"event_id":"sha256:274b72f032d70290b47fa36609a6867a020e1449337a5d5cec9d6c9d8da059a4","target":"graph","created_at":"2026-07-05T05:27:02Z","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/2212.10006/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed in recent years to overcome the adversarial attacks. In this paper, we propose a multi-head uncertainty inference (MH-UI) framework for detecting adversarial attack examples. We adopt a multi-head architecture with multiple prediction heads (i.e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI. ","authors_text":"Jiyang Xie. Zhongwei Si, Kai Guo, Ke Zhang, Kongming Liang, Songyun Yang, Yuqi Yang","cross_cats":["cs.CR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T05:35:15Z","title":"Multi-head Uncertainty Inference for Adversarial Attack Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10006","kind":"arxiv","version":1},"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":"sha256:5c0465090f105e1df22328dbff96d79f94860c16d502b70cba88995695cb0551","target":"record","created_at":"2026-07-05T05:27:02Z","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":"1d6be445e6651a8301655236155365d664704632680713503d5de2bcce3d022c","cross_cats_sorted":["cs.CR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-12-20T05:35:15Z","title_canon_sha256":"e1cb941b191e6fa12aa76e667e79086be04663958313b25662f1dc94e57ac0fc"},"schema_version":"1.0","source":{"id":"2212.10006","kind":"arxiv","version":1}},"canonical_sha256":"41cf01f2659cac30077ab21c9844a008b9f8df14f0ce672b340c099e792ef5cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"41cf01f2659cac30077ab21c9844a008b9f8df14f0ce672b340c099e792ef5cb","first_computed_at":"2026-07-05T05:27:02.669163Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:27:02.669163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X5Y8ucfHKdyZnAD4MZpkxTifH55I5gMldlgYGiSNhOSQp71nhLtfNgb9L7BpSNvjRwBiq0cx53WtH/FnO7TjDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:27:02.669522Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.10006","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5c0465090f105e1df22328dbff96d79f94860c16d502b70cba88995695cb0551","sha256:274b72f032d70290b47fa36609a6867a020e1449337a5d5cec9d6c9d8da059a4"],"state_sha256":"060343337ff0004650f93a76a712daf4f854df61edac9e076f687816136b87c2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/gfZ+FUVpdqGa7SSavCHm69CGg/bi+AnuDZASghY4JhCjLIrTkxvzFsPE8suKqGNCUw7oWAvMpeYgCuGPPIiAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T16:47:34.110183Z","bundle_sha256":"7abd325d84f1f726d92c12b469fa20cc505dc2165a1a282fe99bc68d72ca80a0"}}