{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:WR2CP2ONSU65CXGQO7IRCMSTIX","short_pith_number":"pith:WR2CP2ON","schema_version":"1.0","canonical_sha256":"b47427e9cd953dd15cd077d111325345dcc01e785c9c59372e19e991c6eb907d","source":{"kind":"arxiv","id":"1909.06122","version":3},"attestation_state":"computed","paper":{"title":"FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Felix Juefei-Xu, Jian Wang, Lei Ma, Run Wang, Xiaofei Xie, Yang Liu, Yihao Huang","submitted_at":"2019-09-13T10:08:44Z","abstract_excerpt":"In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation. However, robust detectors of these AI-synthesized fake faces are still in their infancy and are not ready to fully tackle this emerging challenge. In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces. The studies on neuron coverage and i"},"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":"1909.06122","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-09-13T10:08:44Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"432bb99af49807017be5fb3b4429f18f1f50a7bfe31243d5eab4455e81acae53","abstract_canon_sha256":"060922d116ad69a29043398722bfc041ed0f79ddc9e92db3550f4e224625d798"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:19:36.381635Z","signature_b64":"CTxfJyLWOmRDbWvP3md7olE8rXlUX6MaUrpKFp17wA9HAjTmM2kmRAes1ZSHdQdCT7TKoRdVrr3Q6Y1PuSTyBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b47427e9cd953dd15cd077d111325345dcc01e785c9c59372e19e991c6eb907d","last_reissued_at":"2026-07-05T01:19:36.381105Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:19:36.381105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Felix Juefei-Xu, Jian Wang, Lei Ma, Run Wang, Xiaofei Xie, Yang Liu, Yihao Huang","submitted_at":"2019-09-13T10:08:44Z","abstract_excerpt":"In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation. However, robust detectors of these AI-synthesized fake faces are still in their infancy and are not ready to fully tackle this emerging challenge. In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces. The studies on neuron coverage and i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.06122","kind":"arxiv","version":3},"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/1909.06122/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":"1909.06122","created_at":"2026-07-05T01:19:36.381174+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.06122v3","created_at":"2026-07-05T01:19:36.381174+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.06122","created_at":"2026-07-05T01:19:36.381174+00:00"},{"alias_kind":"pith_short_12","alias_value":"WR2CP2ONSU65","created_at":"2026-07-05T01:19:36.381174+00:00"},{"alias_kind":"pith_short_16","alias_value":"WR2CP2ONSU65CXGQ","created_at":"2026-07-05T01:19:36.381174+00:00"},{"alias_kind":"pith_short_8","alias_value":"WR2CP2ON","created_at":"2026-07-05T01:19:36.381174+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2411.15633","citing_title":"Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection","ref_index":287,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04086","citing_title":"LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.27590","citing_title":"Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering","ref_index":49,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX","json":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX.json","graph_json":"https://pith.science/api/pith-number/WR2CP2ONSU65CXGQO7IRCMSTIX/graph.json","events_json":"https://pith.science/api/pith-number/WR2CP2ONSU65CXGQO7IRCMSTIX/events.json","paper":"https://pith.science/paper/WR2CP2ON"},"agent_actions":{"view_html":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX","download_json":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX.json","view_paper":"https://pith.science/paper/WR2CP2ON","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.06122&json=true","fetch_graph":"https://pith.science/api/pith-number/WR2CP2ONSU65CXGQO7IRCMSTIX/graph.json","fetch_events":"https://pith.science/api/pith-number/WR2CP2ONSU65CXGQO7IRCMSTIX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX/action/storage_attestation","attest_author":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX/action/author_attestation","sign_citation":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX/action/citation_signature","submit_replication":"https://pith.science/pith/WR2CP2ONSU65CXGQO7IRCMSTIX/action/replication_record"}},"created_at":"2026-07-05T01:19:36.381174+00:00","updated_at":"2026-07-05T01:19:36.381174+00:00"}