{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VRYOJ7CBMHUZVFCV7DJ3KOK6C2","short_pith_number":"pith:VRYOJ7CB","schema_version":"1.0","canonical_sha256":"ac70e4fc4161e99a9455f8d3b5395e16bda0fd9a483c8e85e46d762318526742","source":{"kind":"arxiv","id":"2308.06217","version":1},"attestation_state":"computed","paper":{"title":"Continual Face Forgery Detection via Historical Distribution Preserving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ke Sun, Rongrong Ji, Shen Chen, Shouhong Ding, Taiping Yao, Xiaoshuai Sun","submitted_at":"2023-08-11T16:37:31Z","abstract_excerpt":"Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall short of practical application. Additionally, finetuning these methods on historical training data is resource-intensive in terms of time and storage. In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones. Specifically, we propose a Historical Distribution Preserving (HDP) framework t"},"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":"2308.06217","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-11T16:37:31Z","cross_cats_sorted":[],"title_canon_sha256":"55352850991241a15031f0353b991f36cac40104f92263da571c2f5e6b4252a9","abstract_canon_sha256":"7d885f025a0a05ae649167230113aa69bf9c379112b164369566b8d8a1a20897"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:40:24.846849Z","signature_b64":"2QZ8a3B9tgM2l5CM0ZxJFaX7nVNQ6ZvGv65xUDQ65EBNDwsc5IvY+RdR3Kpt2j+R9jauAxYmQHpUAhcGArOpCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac70e4fc4161e99a9455f8d3b5395e16bda0fd9a483c8e85e46d762318526742","last_reissued_at":"2026-07-05T06:40:24.846399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:40:24.846399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continual Face Forgery Detection via Historical Distribution Preserving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ke Sun, Rongrong Ji, Shen Chen, Shouhong Ding, Taiping Yao, Xiaoshuai Sun","submitted_at":"2023-08-11T16:37:31Z","abstract_excerpt":"Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall short of practical application. Additionally, finetuning these methods on historical training data is resource-intensive in terms of time and storage. In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones. Specifically, we propose a Historical Distribution Preserving (HDP) framework t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.06217","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/2308.06217/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":"2308.06217","created_at":"2026-07-05T06:40:24.846454+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.06217v1","created_at":"2026-07-05T06:40:24.846454+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.06217","created_at":"2026-07-05T06:40:24.846454+00:00"},{"alias_kind":"pith_short_12","alias_value":"VRYOJ7CBMHUZ","created_at":"2026-07-05T06:40:24.846454+00:00"},{"alias_kind":"pith_short_16","alias_value":"VRYOJ7CBMHUZVFCV","created_at":"2026-07-05T06:40:24.846454+00:00"},{"alias_kind":"pith_short_8","alias_value":"VRYOJ7CB","created_at":"2026-07-05T06:40:24.846454+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/VRYOJ7CBMHUZVFCV7DJ3KOK6C2","json":"https://pith.science/pith/VRYOJ7CBMHUZVFCV7DJ3KOK6C2.json","graph_json":"https://pith.science/api/pith-number/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/graph.json","events_json":"https://pith.science/api/pith-number/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/events.json","paper":"https://pith.science/paper/VRYOJ7CB"},"agent_actions":{"view_html":"https://pith.science/pith/VRYOJ7CBMHUZVFCV7DJ3KOK6C2","download_json":"https://pith.science/pith/VRYOJ7CBMHUZVFCV7DJ3KOK6C2.json","view_paper":"https://pith.science/paper/VRYOJ7CB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.06217&json=true","fetch_graph":"https://pith.science/api/pith-number/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/graph.json","fetch_events":"https://pith.science/api/pith-number/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/action/storage_attestation","attest_author":"https://pith.science/pith/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/action/author_attestation","sign_citation":"https://pith.science/pith/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/action/citation_signature","submit_replication":"https://pith.science/pith/VRYOJ7CBMHUZVFCV7DJ3KOK6C2/action/replication_record"}},"created_at":"2026-07-05T06:40:24.846454+00:00","updated_at":"2026-07-05T06:40:24.846454+00:00"}