{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:J6KQSQ6JUUYL27C4DQBC66CTVU","short_pith_number":"pith:J6KQSQ6J","schema_version":"1.0","canonical_sha256":"4f950943c9a530bd7c5c1c022f7853ad347b29c150441328e87d06af3442242e","source":{"kind":"arxiv","id":"2508.10771","version":1},"attestation_state":"computed","paper":{"title":"AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jieyu Li, Joey Tianyi Zhou, Xin Zhang","submitted_at":"2025-08-14T15:55:49Z","abstract_excerpt":"Recent advances in AI-generated content have fueled the rise of highly realistic synthetic videos, posing severe risks to societal trust and digital integrity. Existing benchmarks for video authenticity detection typically suffer from limited realism, insufficient scale, and inadequate complexity, failing to effectively evaluate modern vision-language models against sophisticated forgeries. To address this critical gap, we introduce AEGIS, a novel large-scale benchmark explicitly targeting the detection of hyper-realistic and semantically nuanced AI-generated videos. AEGIS comprises over 10,00"},"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":"2508.10771","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-14T15:55:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e95bedfc793e59e15340815b306398d0fb0a9b13e77936d8b1cf4df20fabd022","abstract_canon_sha256":"21bdbf9eb7e9844510c59aa620bd028c01ee8b68cc3875309ad31fd9770e7aa1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:02.275404Z","signature_b64":"XGKRvMgVxBx0c27TSshtkdRHgy5t2gWrWRBhquQqfZ/q5fwW2hpXjT7HbgIygQ3wtpuwfVoExYKg+0CDKuQDAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f950943c9a530bd7c5c1c022f7853ad347b29c150441328e87d06af3442242e","last_reissued_at":"2026-07-05T11:54:02.275048Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:02.275048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Jieyu Li, Joey Tianyi Zhou, Xin Zhang","submitted_at":"2025-08-14T15:55:49Z","abstract_excerpt":"Recent advances in AI-generated content have fueled the rise of highly realistic synthetic videos, posing severe risks to societal trust and digital integrity. Existing benchmarks for video authenticity detection typically suffer from limited realism, insufficient scale, and inadequate complexity, failing to effectively evaluate modern vision-language models against sophisticated forgeries. To address this critical gap, we introduce AEGIS, a novel large-scale benchmark explicitly targeting the detection of hyper-realistic and semantically nuanced AI-generated videos. AEGIS comprises over 10,00"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.10771","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/2508.10771/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":"2508.10771","created_at":"2026-07-05T11:54:02.275104+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.10771v1","created_at":"2026-07-05T11:54:02.275104+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.10771","created_at":"2026-07-05T11:54:02.275104+00:00"},{"alias_kind":"pith_short_12","alias_value":"J6KQSQ6JUUYL","created_at":"2026-07-05T11:54:02.275104+00:00"},{"alias_kind":"pith_short_16","alias_value":"J6KQSQ6JUUYL27C4","created_at":"2026-07-05T11:54:02.275104+00:00"},{"alias_kind":"pith_short_8","alias_value":"J6KQSQ6J","created_at":"2026-07-05T11:54:02.275104+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.10248","citing_title":"RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU","json":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU.json","graph_json":"https://pith.science/api/pith-number/J6KQSQ6JUUYL27C4DQBC66CTVU/graph.json","events_json":"https://pith.science/api/pith-number/J6KQSQ6JUUYL27C4DQBC66CTVU/events.json","paper":"https://pith.science/paper/J6KQSQ6J"},"agent_actions":{"view_html":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU","download_json":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU.json","view_paper":"https://pith.science/paper/J6KQSQ6J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.10771&json=true","fetch_graph":"https://pith.science/api/pith-number/J6KQSQ6JUUYL27C4DQBC66CTVU/graph.json","fetch_events":"https://pith.science/api/pith-number/J6KQSQ6JUUYL27C4DQBC66CTVU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU/action/storage_attestation","attest_author":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU/action/author_attestation","sign_citation":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU/action/citation_signature","submit_replication":"https://pith.science/pith/J6KQSQ6JUUYL27C4DQBC66CTVU/action/replication_record"}},"created_at":"2026-07-05T11:54:02.275104+00:00","updated_at":"2026-07-05T11:54:02.275104+00:00"}