{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AAFXSKYGWDX3P55CAMN4AIDLLA","short_pith_number":"pith:AAFXSKYG","schema_version":"1.0","canonical_sha256":"000b792b06b0efb7f7a2031bc0206b5800302043199da779cabbda187277236a","source":{"kind":"arxiv","id":"2411.17209","version":1},"attestation_state":"computed","paper":{"title":"LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Harry Cheng, Mengxiao Huang, Tianyi Wang, Xiao Zhang, Zhiqi Shen","submitted_at":"2024-11-26T08:24:56Z","abstract_excerpt":"Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of De"},"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":"2411.17209","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-26T08:24:56Z","cross_cats_sorted":[],"title_canon_sha256":"808db24f510a7d0b15fa063e5251a2bc7a7c29050aee367545530faba461c5b5","abstract_canon_sha256":"c2e472c8875b094741f5f06c42447e80c2b926482df6fe4ed07414a5c04e044f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:29.096141Z","signature_b64":"Jj+dRBYgioAXurCU3LKLaJ74iY6La1mY6wmWHlBoLH4rkuAqfaSaIV8ts7nvLbE9DySh2Sgzkg4VRppAg4v/Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"000b792b06b0efb7f7a2031bc0206b5800302043199da779cabbda187277236a","last_reissued_at":"2026-07-05T09:40:29.095700Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:29.095700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Harry Cheng, Mengxiao Huang, Tianyi Wang, Xiao Zhang, Zhiqi Shen","submitted_at":"2024-11-26T08:24:56Z","abstract_excerpt":"Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of De"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.17209","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/2411.17209/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":"2411.17209","created_at":"2026-07-05T09:40:29.095753+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.17209v1","created_at":"2026-07-05T09:40:29.095753+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.17209","created_at":"2026-07-05T09:40:29.095753+00:00"},{"alias_kind":"pith_short_12","alias_value":"AAFXSKYGWDX3","created_at":"2026-07-05T09:40:29.095753+00:00"},{"alias_kind":"pith_short_16","alias_value":"AAFXSKYGWDX3P55C","created_at":"2026-07-05T09:40:29.095753+00:00"},{"alias_kind":"pith_short_8","alias_value":"AAFXSKYG","created_at":"2026-07-05T09:40:29.095753+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/AAFXSKYGWDX3P55CAMN4AIDLLA","json":"https://pith.science/pith/AAFXSKYGWDX3P55CAMN4AIDLLA.json","graph_json":"https://pith.science/api/pith-number/AAFXSKYGWDX3P55CAMN4AIDLLA/graph.json","events_json":"https://pith.science/api/pith-number/AAFXSKYGWDX3P55CAMN4AIDLLA/events.json","paper":"https://pith.science/paper/AAFXSKYG"},"agent_actions":{"view_html":"https://pith.science/pith/AAFXSKYGWDX3P55CAMN4AIDLLA","download_json":"https://pith.science/pith/AAFXSKYGWDX3P55CAMN4AIDLLA.json","view_paper":"https://pith.science/paper/AAFXSKYG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.17209&json=true","fetch_graph":"https://pith.science/api/pith-number/AAFXSKYGWDX3P55CAMN4AIDLLA/graph.json","fetch_events":"https://pith.science/api/pith-number/AAFXSKYGWDX3P55CAMN4AIDLLA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AAFXSKYGWDX3P55CAMN4AIDLLA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AAFXSKYGWDX3P55CAMN4AIDLLA/action/storage_attestation","attest_author":"https://pith.science/pith/AAFXSKYGWDX3P55CAMN4AIDLLA/action/author_attestation","sign_citation":"https://pith.science/pith/AAFXSKYGWDX3P55CAMN4AIDLLA/action/citation_signature","submit_replication":"https://pith.science/pith/AAFXSKYGWDX3P55CAMN4AIDLLA/action/replication_record"}},"created_at":"2026-07-05T09:40:29.095753+00:00","updated_at":"2026-07-05T09:40:29.095753+00:00"}