{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:R7AZ4FPIDRCXQ6DMCX7KEQWOWD","short_pith_number":"pith:R7AZ4FPI","schema_version":"1.0","canonical_sha256":"8fc19e15e81c4578786c15fea242ceb0eb73a5bdf42df10f259a78634c5db4c3","source":{"kind":"arxiv","id":"2607.08156","version":1},"attestation_state":"computed","paper":{"title":"Unified Face Attack Detection via Fine-Grained Semantic Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dingheng Zeng, Haifeng Shen, Haiyang Yi, Ning Jiang, Shijie Yu, Yanhong Liu, Ying Li","submitted_at":"2026-07-09T06:50:31Z","abstract_excerpt":"The growing applications of facial recognition systems are accompanied by increasingly diverse security threats. Existing datasets lack detailed textual descriptions of forgery cues, leading most prior methods to treat face attack detection primarily as a visual recognition task. In this paper, building upon the large-scale MS-UFAD dataset which contains over 8 million attack images, we enrich each image with a fine-grained textual description of forgery cues. Furthermore, we propose a Dual Alignment Forgery Network(DAF-Net) to better leverage these textual information. Extensive experiments d"},"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":"2607.08156","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-09T06:50:31Z","cross_cats_sorted":[],"title_canon_sha256":"928f25498b631de6525d10fd0f8049865d9ecba8c0a1fbe005f39b261c02d9f8","abstract_canon_sha256":"bf632f113ace79a124966e7cdb5d3b069639887f0270931e40a636b661fbe502"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-10T01:19:28.221888Z","signature_b64":"bDoGoP4Zt3OF/qENx7fruua1RkTPfbarKq182EksOqbBt87ZLkjk4VDCqLd7SWYDxL0tL+Iez8WRQmhX9OvDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fc19e15e81c4578786c15fea242ceb0eb73a5bdf42df10f259a78634c5db4c3","last_reissued_at":"2026-07-10T01:19:28.221470Z","signature_status":"signed_v1","first_computed_at":"2026-07-10T01:19:28.221470Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unified Face Attack Detection via Fine-Grained Semantic Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dingheng Zeng, Haifeng Shen, Haiyang Yi, Ning Jiang, Shijie Yu, Yanhong Liu, Ying Li","submitted_at":"2026-07-09T06:50:31Z","abstract_excerpt":"The growing applications of facial recognition systems are accompanied by increasingly diverse security threats. Existing datasets lack detailed textual descriptions of forgery cues, leading most prior methods to treat face attack detection primarily as a visual recognition task. In this paper, building upon the large-scale MS-UFAD dataset which contains over 8 million attack images, we enrich each image with a fine-grained textual description of forgery cues. Furthermore, we propose a Dual Alignment Forgery Network(DAF-Net) to better leverage these textual information. Extensive experiments d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.08156","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/2607.08156/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":"2607.08156","created_at":"2026-07-10T01:19:28.221528+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.08156v1","created_at":"2026-07-10T01:19:28.221528+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.08156","created_at":"2026-07-10T01:19:28.221528+00:00"},{"alias_kind":"pith_short_12","alias_value":"R7AZ4FPIDRCX","created_at":"2026-07-10T01:19:28.221528+00:00"},{"alias_kind":"pith_short_16","alias_value":"R7AZ4FPIDRCXQ6DM","created_at":"2026-07-10T01:19:28.221528+00:00"},{"alias_kind":"pith_short_8","alias_value":"R7AZ4FPI","created_at":"2026-07-10T01:19:28.221528+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/R7AZ4FPIDRCXQ6DMCX7KEQWOWD","json":"https://pith.science/pith/R7AZ4FPIDRCXQ6DMCX7KEQWOWD.json","graph_json":"https://pith.science/api/pith-number/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/graph.json","events_json":"https://pith.science/api/pith-number/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/events.json","paper":"https://pith.science/paper/R7AZ4FPI"},"agent_actions":{"view_html":"https://pith.science/pith/R7AZ4FPIDRCXQ6DMCX7KEQWOWD","download_json":"https://pith.science/pith/R7AZ4FPIDRCXQ6DMCX7KEQWOWD.json","view_paper":"https://pith.science/paper/R7AZ4FPI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.08156&json=true","fetch_graph":"https://pith.science/api/pith-number/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/graph.json","fetch_events":"https://pith.science/api/pith-number/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/action/storage_attestation","attest_author":"https://pith.science/pith/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/action/author_attestation","sign_citation":"https://pith.science/pith/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/action/citation_signature","submit_replication":"https://pith.science/pith/R7AZ4FPIDRCXQ6DMCX7KEQWOWD/action/replication_record"}},"created_at":"2026-07-10T01:19:28.221528+00:00","updated_at":"2026-07-10T01:19:28.221528+00:00"}