{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MQKX34U5JZCHJPQE24MH7FBSTU","short_pith_number":"pith:MQKX34U5","schema_version":"1.0","canonical_sha256":"64157df29d4e4474be04d7187f94329d21f3eb52ee8f58abdc9a79d9d9ea77dd","source":{"kind":"arxiv","id":"2503.15867","version":3},"attestation_state":"computed","paper":{"title":"TruthLens: Visual Grounding for Universal DeepFake Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Amit K. Roy-Chowdhury, Athula Balachandran, Rohit Kundu, Shan Jia, Vishal Mohanty","submitted_at":"2025-03-20T05:40:42Z","abstract_excerpt":"Detecting DeepFakes has become a crucial research area as the widespread use of AI image generators enables the effortless creation of face-manipulated and fully synthetic content, while existing methods are often limited to binary classification (real vs. fake) and lack interpretability. To address these challenges, we propose TruthLens, a novel, unified, and highly generalizable framework that goes beyond traditional binary classification, providing detailed, textual reasoning for its predictions. Distinct from conventional methods, TruthLens performs MLLM grounding.\n  TruthLens uses a task-"},"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":"2503.15867","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-20T05:40:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3b4d75e951d3afb39895b674907ecb408eee823341388d2a9eccc54aaffbaa73","abstract_canon_sha256":"3acff32f5fd278c81bd81f2c8751bee156f68fe112d59a447275f34b71cd2635"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:46.823151Z","signature_b64":"nzzQPTIAML6YfDU2flIeJM+yQlPkMyoUzux2J9qHPAckjWc3I5yQ6gbrSHTYsmqnqQnMbwjO8YpGAXt/BQD2Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64157df29d4e4474be04d7187f94329d21f3eb52ee8f58abdc9a79d9d9ea77dd","last_reissued_at":"2026-07-05T12:03:46.822651Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:46.822651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TruthLens: Visual Grounding for Universal DeepFake Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Amit K. Roy-Chowdhury, Athula Balachandran, Rohit Kundu, Shan Jia, Vishal Mohanty","submitted_at":"2025-03-20T05:40:42Z","abstract_excerpt":"Detecting DeepFakes has become a crucial research area as the widespread use of AI image generators enables the effortless creation of face-manipulated and fully synthetic content, while existing methods are often limited to binary classification (real vs. fake) and lack interpretability. To address these challenges, we propose TruthLens, a novel, unified, and highly generalizable framework that goes beyond traditional binary classification, providing detailed, textual reasoning for its predictions. Distinct from conventional methods, TruthLens performs MLLM grounding.\n  TruthLens uses a task-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.15867","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/2503.15867/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":"2503.15867","created_at":"2026-07-05T12:03:46.822710+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.15867v3","created_at":"2026-07-05T12:03:46.822710+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.15867","created_at":"2026-07-05T12:03:46.822710+00:00"},{"alias_kind":"pith_short_12","alias_value":"MQKX34U5JZCH","created_at":"2026-07-05T12:03:46.822710+00:00"},{"alias_kind":"pith_short_16","alias_value":"MQKX34U5JZCHJPQE","created_at":"2026-07-05T12:03:46.822710+00:00"},{"alias_kind":"pith_short_8","alias_value":"MQKX34U5","created_at":"2026-07-05T12:03:46.822710+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.13660","citing_title":"VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU","json":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU.json","graph_json":"https://pith.science/api/pith-number/MQKX34U5JZCHJPQE24MH7FBSTU/graph.json","events_json":"https://pith.science/api/pith-number/MQKX34U5JZCHJPQE24MH7FBSTU/events.json","paper":"https://pith.science/paper/MQKX34U5"},"agent_actions":{"view_html":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU","download_json":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU.json","view_paper":"https://pith.science/paper/MQKX34U5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.15867&json=true","fetch_graph":"https://pith.science/api/pith-number/MQKX34U5JZCHJPQE24MH7FBSTU/graph.json","fetch_events":"https://pith.science/api/pith-number/MQKX34U5JZCHJPQE24MH7FBSTU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU/action/storage_attestation","attest_author":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU/action/author_attestation","sign_citation":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU/action/citation_signature","submit_replication":"https://pith.science/pith/MQKX34U5JZCHJPQE24MH7FBSTU/action/replication_record"}},"created_at":"2026-07-05T12:03:46.822710+00:00","updated_at":"2026-07-05T12:03:46.822710+00:00"}