{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:YNX7EAV25CGVDKWL5QI5KFXMWJ","short_pith_number":"pith:YNX7EAV2","canonical_record":{"source":{"id":"1408.5601","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-08-24T13:08:19Z","cross_cats_sorted":[],"title_canon_sha256":"f4ff3f2e90846fecb8e45b7c6b3f1ad939c899fa70e2f551bc374e8c62c748f2","abstract_canon_sha256":"60c3a69cc19bccc6276771e769c109c3127899645631979be759795efbc11ccd"},"schema_version":"1.0"},"canonical_sha256":"c36ff202bae88d51aacbec11d516ecb25a7a3cd0e03b35e88756c4272329bacd","source":{"kind":"arxiv","id":"1408.5601","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1408.5601","created_at":"2026-05-18T02:44:15Z"},{"alias_kind":"arxiv_version","alias_value":"1408.5601v2","created_at":"2026-05-18T02:44:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.5601","created_at":"2026-05-18T02:44:15Z"},{"alias_kind":"pith_short_12","alias_value":"YNX7EAV25CGV","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"YNX7EAV25CGVDKWL","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"YNX7EAV2","created_at":"2026-05-18T12:28:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:YNX7EAV25CGVDKWL5QI5KFXMWJ","target":"record","payload":{"canonical_record":{"source":{"id":"1408.5601","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-08-24T13:08:19Z","cross_cats_sorted":[],"title_canon_sha256":"f4ff3f2e90846fecb8e45b7c6b3f1ad939c899fa70e2f551bc374e8c62c748f2","abstract_canon_sha256":"60c3a69cc19bccc6276771e769c109c3127899645631979be759795efbc11ccd"},"schema_version":"1.0"},"canonical_sha256":"c36ff202bae88d51aacbec11d516ecb25a7a3cd0e03b35e88756c4272329bacd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:15.972186Z","signature_b64":"zgByePro5iGBrHs/zsQA7WA2Fs5MrdK1ByJarZFFrOjF8RAg3VYTyliBMRLfhma25ZPaAQ0kptrxawFqFPgPAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c36ff202bae88d51aacbec11d516ecb25a7a3cd0e03b35e88756c4272329bacd","last_reissued_at":"2026-05-18T02:44:15.971626Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:15.971626Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1408.5601","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":"2026-05-18T02:44:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wt9s4KJVPnnTKZZgTLhf32PIIgEtE8zBqdK5roFtl4uwkE4xqzcMT7hpzqp9btqTSzb/FVdyOOHQUyhMKVWTAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T00:43:41.740846Z"},"content_sha256":"4935086982d25e7a1f54e1281e686f26f1ac25020ba981d9adbe922ee7ceb881","schema_version":"1.0","event_id":"sha256:4935086982d25e7a1f54e1281e686f26f1ac25020ba981d9adbe922ee7ceb881"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:YNX7EAV25CGVDKWL5QI5KFXMWJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learn Convolutional Neural Network for Face Anti-Spoofing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianwei Yang, Stan Z. Li, Zhen Lei","submitted_at":"2014-08-24T13:08:19Z","abstract_excerpt":"Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some data pre-processing, the face anti-spoofing performance improves drastically. In the experiments, over 70% relative decrease of Half Total Error Rate (HTER) is achieved on two challenging datasets, CASI"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.5601","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":""},"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":"2026-05-18T02:44:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EhK82MWZMHo8yfeban7MxbbyyQfzuPrNIlAFyaO8PBHFn/qeRTLoTNLlYte+iExh+HGTkv76wAkd4UT3etAbAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T00:43:41.741194Z"},"content_sha256":"46a19a2be228120ae3f8ab63b0f1216b78e89ba3dce30c8e7cb6ca26cf8e4675","schema_version":"1.0","event_id":"sha256:46a19a2be228120ae3f8ab63b0f1216b78e89ba3dce30c8e7cb6ca26cf8e4675"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YNX7EAV25CGVDKWL5QI5KFXMWJ/bundle.json","state_url":"https://pith.science/pith/YNX7EAV25CGVDKWL5QI5KFXMWJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YNX7EAV25CGVDKWL5QI5KFXMWJ/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":"2026-06-05T00:43:41Z","links":{"resolver":"https://pith.science/pith/YNX7EAV25CGVDKWL5QI5KFXMWJ","bundle":"https://pith.science/pith/YNX7EAV25CGVDKWL5QI5KFXMWJ/bundle.json","state":"https://pith.science/pith/YNX7EAV25CGVDKWL5QI5KFXMWJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YNX7EAV25CGVDKWL5QI5KFXMWJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:YNX7EAV25CGVDKWL5QI5KFXMWJ","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":"60c3a69cc19bccc6276771e769c109c3127899645631979be759795efbc11ccd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-08-24T13:08:19Z","title_canon_sha256":"f4ff3f2e90846fecb8e45b7c6b3f1ad939c899fa70e2f551bc374e8c62c748f2"},"schema_version":"1.0","source":{"id":"1408.5601","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1408.5601","created_at":"2026-05-18T02:44:15Z"},{"alias_kind":"arxiv_version","alias_value":"1408.5601v2","created_at":"2026-05-18T02:44:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1408.5601","created_at":"2026-05-18T02:44:15Z"},{"alias_kind":"pith_short_12","alias_value":"YNX7EAV25CGV","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"YNX7EAV25CGVDKWL","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"YNX7EAV2","created_at":"2026-05-18T12:28:57Z"}],"graph_snapshots":[{"event_id":"sha256:46a19a2be228120ae3f8ab63b0f1216b78e89ba3dce30c8e7cb6ca26cf8e4675","target":"graph","created_at":"2026-05-18T02:44:15Z","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"},"paper":{"abstract_excerpt":"Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some data pre-processing, the face anti-spoofing performance improves drastically. In the experiments, over 70% relative decrease of Half Total Error Rate (HTER) is achieved on two challenging datasets, CASI","authors_text":"Jianwei Yang, Stan Z. Li, Zhen Lei","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-08-24T13:08:19Z","title":"Learn Convolutional Neural Network for Face Anti-Spoofing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1408.5601","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":"sha256:4935086982d25e7a1f54e1281e686f26f1ac25020ba981d9adbe922ee7ceb881","target":"record","created_at":"2026-05-18T02:44:15Z","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":"60c3a69cc19bccc6276771e769c109c3127899645631979be759795efbc11ccd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-08-24T13:08:19Z","title_canon_sha256":"f4ff3f2e90846fecb8e45b7c6b3f1ad939c899fa70e2f551bc374e8c62c748f2"},"schema_version":"1.0","source":{"id":"1408.5601","kind":"arxiv","version":2}},"canonical_sha256":"c36ff202bae88d51aacbec11d516ecb25a7a3cd0e03b35e88756c4272329bacd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c36ff202bae88d51aacbec11d516ecb25a7a3cd0e03b35e88756c4272329bacd","first_computed_at":"2026-05-18T02:44:15.971626Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:44:15.971626Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zgByePro5iGBrHs/zsQA7WA2Fs5MrdK1ByJarZFFrOjF8RAg3VYTyliBMRLfhma25ZPaAQ0kptrxawFqFPgPAA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:44:15.972186Z","signed_message":"canonical_sha256_bytes"},"source_id":"1408.5601","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4935086982d25e7a1f54e1281e686f26f1ac25020ba981d9adbe922ee7ceb881","sha256:46a19a2be228120ae3f8ab63b0f1216b78e89ba3dce30c8e7cb6ca26cf8e4675"],"state_sha256":"1f2992ad5300d99fb9790223a6ce0459a1a1b4c16038d2a120d4568a10885617"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Aq9Ns3tO1GiS8LxPIcVedC24B5Bt0fLA7K9ggMEoJVbJ5Jm6jN+Vz8Dp6tJ69q90hxNF38S3XRfnvmau0gQlCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T00:43:41.743305Z","bundle_sha256":"117e430548f4952178562ff499c2aa09f3f667e4decb15427ff615b568ebb874"}}