{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:LPSTOVVDPHENHUSHDZDB2KGD7E","short_pith_number":"pith:LPSTOVVD","canonical_record":{"source":{"id":"2312.13630","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T07:48:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"380ecb93578014edcbd6127e2da336882731b90b6a0168c9360b7e40a9f789d0","abstract_canon_sha256":"797715434c885bb903a830fce8d3c631e9b50166b0fcd2466f3eff7d5cb237f0"},"schema_version":"1.0"},"canonical_sha256":"5be53756a379c8d3d2471e461d28c3f92d294da1f761731a52945669583c332d","source":{"kind":"arxiv","id":"2312.13630","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.13630","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"arxiv_version","alias_value":"2312.13630v1","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.13630","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"pith_short_12","alias_value":"LPSTOVVDPHEN","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"pith_short_16","alias_value":"LPSTOVVDPHENHUSH","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"pith_short_8","alias_value":"LPSTOVVD","created_at":"2026-07-05T07:26:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:LPSTOVVDPHENHUSHDZDB2KGD7E","target":"record","payload":{"canonical_record":{"source":{"id":"2312.13630","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T07:48:15Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"380ecb93578014edcbd6127e2da336882731b90b6a0168c9360b7e40a9f789d0","abstract_canon_sha256":"797715434c885bb903a830fce8d3c631e9b50166b0fcd2466f3eff7d5cb237f0"},"schema_version":"1.0"},"canonical_sha256":"5be53756a379c8d3d2471e461d28c3f92d294da1f761731a52945669583c332d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:47.688091Z","signature_b64":"hdpnQ9BVdNKNb59XnMVi+zmGy7Ikl8sq85HElWwi2I6cmtO2zAGRFyyR30MSU2l7g98gbWnfSAYqY2jibJbjCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5be53756a379c8d3d2471e461d28c3f92d294da1f761731a52945669583c332d","last_reissued_at":"2026-07-05T07:26:47.687587Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:47.687587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.13630","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":"2026-07-05T07:26:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l/yLFGq9MNB6g+DNVETERwOFkmb9MR14/yejD5PCZ8k1FSMBrs3ZkkUXLwp2pmMLLinAt6ynPJUcb69Tp9fjBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T17:30:47.168519Z"},"content_sha256":"bd7bbd695c3b0c34c73c06146016a9996eec0d46480d51f7dc6259b8beeae536","schema_version":"1.0","event_id":"sha256:bd7bbd695c3b0c34c73c06146016a9996eec0d46480d51f7dc6259b8beeae536"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:LPSTOVVDPHENHUSHDZDB2KGD7E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Dongxiao Zhu, Huaming Chen, Jiayu Zhang, Kim-Kwang Raymond Choo, Minhui Xue, Xinyi Wang, Zhibo Jin, Zhiyu Zhu","submitted_at":"2023-12-21T07:48:15Z","abstract_excerpt":"To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the axioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the existing attribution methods present challenges for effective interpretation and efficient computation. In this work, we introduce MFABA, an attribution algorithm that adheres to axioms,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.13630","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/2312.13630/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":"2026-07-05T07:26:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cFjuJ7pK1R+aOGeJhHVKO8+ODoaereqqtBtRZuoYmLVDvo466i2FkTr1z6S7Dp34/zDaj4B9VVmjQzY3axrODg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T17:30:47.169020Z"},"content_sha256":"d70b04b4acc46fa4e51a7109a3ec85efa61a6603f72ce17ad48da3ed6e13ec5f","schema_version":"1.0","event_id":"sha256:d70b04b4acc46fa4e51a7109a3ec85efa61a6603f72ce17ad48da3ed6e13ec5f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LPSTOVVDPHENHUSHDZDB2KGD7E/bundle.json","state_url":"https://pith.science/pith/LPSTOVVDPHENHUSHDZDB2KGD7E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LPSTOVVDPHENHUSHDZDB2KGD7E/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":"2026-08-13T17:30:47Z","links":{"resolver":"https://pith.science/pith/LPSTOVVDPHENHUSHDZDB2KGD7E","bundle":"https://pith.science/pith/LPSTOVVDPHENHUSHDZDB2KGD7E/bundle.json","state":"https://pith.science/pith/LPSTOVVDPHENHUSHDZDB2KGD7E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LPSTOVVDPHENHUSHDZDB2KGD7E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:LPSTOVVDPHENHUSHDZDB2KGD7E","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":"797715434c885bb903a830fce8d3c631e9b50166b0fcd2466f3eff7d5cb237f0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T07:48:15Z","title_canon_sha256":"380ecb93578014edcbd6127e2da336882731b90b6a0168c9360b7e40a9f789d0"},"schema_version":"1.0","source":{"id":"2312.13630","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.13630","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"arxiv_version","alias_value":"2312.13630v1","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.13630","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"pith_short_12","alias_value":"LPSTOVVDPHEN","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"pith_short_16","alias_value":"LPSTOVVDPHENHUSH","created_at":"2026-07-05T07:26:47Z"},{"alias_kind":"pith_short_8","alias_value":"LPSTOVVD","created_at":"2026-07-05T07:26:47Z"}],"graph_snapshots":[{"event_id":"sha256:d70b04b4acc46fa4e51a7109a3ec85efa61a6603f72ce17ad48da3ed6e13ec5f","target":"graph","created_at":"2026-07-05T07:26:47Z","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/2312.13630/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the axioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the existing attribution methods present challenges for effective interpretation and efficient computation. In this work, we introduce MFABA, an attribution algorithm that adheres to axioms,","authors_text":"Dongxiao Zhu, Huaming Chen, Jiayu Zhang, Kim-Kwang Raymond Choo, Minhui Xue, Xinyi Wang, Zhibo Jin, Zhiyu Zhu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T07:48:15Z","title":"MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.13630","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:bd7bbd695c3b0c34c73c06146016a9996eec0d46480d51f7dc6259b8beeae536","target":"record","created_at":"2026-07-05T07:26:47Z","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":"797715434c885bb903a830fce8d3c631e9b50166b0fcd2466f3eff7d5cb237f0","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-21T07:48:15Z","title_canon_sha256":"380ecb93578014edcbd6127e2da336882731b90b6a0168c9360b7e40a9f789d0"},"schema_version":"1.0","source":{"id":"2312.13630","kind":"arxiv","version":1}},"canonical_sha256":"5be53756a379c8d3d2471e461d28c3f92d294da1f761731a52945669583c332d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5be53756a379c8d3d2471e461d28c3f92d294da1f761731a52945669583c332d","first_computed_at":"2026-07-05T07:26:47.687587Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:26:47.687587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hdpnQ9BVdNKNb59XnMVi+zmGy7Ikl8sq85HElWwi2I6cmtO2zAGRFyyR30MSU2l7g98gbWnfSAYqY2jibJbjCA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:26:47.688091Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.13630","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd7bbd695c3b0c34c73c06146016a9996eec0d46480d51f7dc6259b8beeae536","sha256:d70b04b4acc46fa4e51a7109a3ec85efa61a6603f72ce17ad48da3ed6e13ec5f"],"state_sha256":"b3e568e4f9bbdea61e838ea2053934dd9d10b9b6d2c4bf2dfc00e0beedd31e06"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+emLIWAZNorOHXN/7McC75b4Opud7PJJHoacWOcTRo+l3TeIX6SLcIVbxRl5qKaeut7DlzAfCqvmYY5+s9DkAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T17:30:47.175370Z","bundle_sha256":"d06db45d34d599a087178e1c6434321fcd552628d7db3b94d211576ccdb4b3ba"}}