{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:WR5QYC5OSJEV45TX3UD6IQKWJ2","short_pith_number":"pith:WR5QYC5O","schema_version":"1.0","canonical_sha256":"b47b0c0bae92495e7677dd07e441564e89db2c1d1740f46c0e8e89a5cec6d345","source":{"kind":"arxiv","id":"2206.06103","version":1},"attestation_state":"computed","paper":{"title":"Learning Feature Disentanglement and Dynamic Fusion for Recaptured Image Forensic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hong Jin, Lin Zheng, Shuyu Miao","submitted_at":"2022-06-13T12:47:13Z","abstract_excerpt":"Image recapture seriously breaks the fairness of artificial intelligent (AI) systems, which deceives the system by recapturing others' images. Most of the existing recapture models can only address a single pattern of recapture (e.g., moire, edge, artifact, and others) based on the datasets with simulated recaptured images using fixed electronic devices. In this paper, we explicitly redefine image recapture forensic task as four patterns of image recapture recognition, i.e., moire recapture, edge recapture, artifact recapture, and other recapture. Meanwhile, we propose a novel Feature Disentan"},"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":"2206.06103","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-13T12:47:13Z","cross_cats_sorted":[],"title_canon_sha256":"1af5a7b67365a26900e7e4398761572c8594f3bc03bd23799a4ced9e771717ae","abstract_canon_sha256":"6aeed71b550efdd4c49dd67543a088a3f4092951625ec9d84c237dc4d9d3b92a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:31:15.222369Z","signature_b64":"c5ovlK085tSMoteb8onytb+nMivVYp14k4FMA+Os3GClokHzpjD7dI8BcXGjgWRYQO9t8UGQJcIf+rG6iP7kCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b47b0c0bae92495e7677dd07e441564e89db2c1d1740f46c0e8e89a5cec6d345","last_reissued_at":"2026-07-05T04:31:15.221931Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:31:15.221931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Feature Disentanglement and Dynamic Fusion for Recaptured Image Forensic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hong Jin, Lin Zheng, Shuyu Miao","submitted_at":"2022-06-13T12:47:13Z","abstract_excerpt":"Image recapture seriously breaks the fairness of artificial intelligent (AI) systems, which deceives the system by recapturing others' images. Most of the existing recapture models can only address a single pattern of recapture (e.g., moire, edge, artifact, and others) based on the datasets with simulated recaptured images using fixed electronic devices. In this paper, we explicitly redefine image recapture forensic task as four patterns of image recapture recognition, i.e., moire recapture, edge recapture, artifact recapture, and other recapture. Meanwhile, we propose a novel Feature Disentan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.06103","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/2206.06103/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":"2206.06103","created_at":"2026-07-05T04:31:15.221997+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.06103v1","created_at":"2026-07-05T04:31:15.221997+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.06103","created_at":"2026-07-05T04:31:15.221997+00:00"},{"alias_kind":"pith_short_12","alias_value":"WR5QYC5OSJEV","created_at":"2026-07-05T04:31:15.221997+00:00"},{"alias_kind":"pith_short_16","alias_value":"WR5QYC5OSJEV45TX","created_at":"2026-07-05T04:31:15.221997+00:00"},{"alias_kind":"pith_short_8","alias_value":"WR5QYC5O","created_at":"2026-07-05T04:31:15.221997+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/WR5QYC5OSJEV45TX3UD6IQKWJ2","json":"https://pith.science/pith/WR5QYC5OSJEV45TX3UD6IQKWJ2.json","graph_json":"https://pith.science/api/pith-number/WR5QYC5OSJEV45TX3UD6IQKWJ2/graph.json","events_json":"https://pith.science/api/pith-number/WR5QYC5OSJEV45TX3UD6IQKWJ2/events.json","paper":"https://pith.science/paper/WR5QYC5O"},"agent_actions":{"view_html":"https://pith.science/pith/WR5QYC5OSJEV45TX3UD6IQKWJ2","download_json":"https://pith.science/pith/WR5QYC5OSJEV45TX3UD6IQKWJ2.json","view_paper":"https://pith.science/paper/WR5QYC5O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.06103&json=true","fetch_graph":"https://pith.science/api/pith-number/WR5QYC5OSJEV45TX3UD6IQKWJ2/graph.json","fetch_events":"https://pith.science/api/pith-number/WR5QYC5OSJEV45TX3UD6IQKWJ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WR5QYC5OSJEV45TX3UD6IQKWJ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WR5QYC5OSJEV45TX3UD6IQKWJ2/action/storage_attestation","attest_author":"https://pith.science/pith/WR5QYC5OSJEV45TX3UD6IQKWJ2/action/author_attestation","sign_citation":"https://pith.science/pith/WR5QYC5OSJEV45TX3UD6IQKWJ2/action/citation_signature","submit_replication":"https://pith.science/pith/WR5QYC5OSJEV45TX3UD6IQKWJ2/action/replication_record"}},"created_at":"2026-07-05T04:31:15.221997+00:00","updated_at":"2026-07-05T04:31:15.221997+00:00"}