{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:CXMPRZP6KAB2CIN64UJLEY2BLL","short_pith_number":"pith:CXMPRZP6","canonical_record":{"source":{"id":"2210.08529","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-16T13:30:13Z","cross_cats_sorted":[],"title_canon_sha256":"782d925807524ca2bcaa7b40f4970a2abd6cbc1e79cac242bdd94b082ec6d705","abstract_canon_sha256":"bbdda82d75a8b7f18d663b875df9f586ca087eec89e597f64c897de2caded955"},"schema_version":"1.0"},"canonical_sha256":"15d8f8e5fe5003a121bee512b263415afa9fe9961959a673cc88fe139e7b8d00","source":{"kind":"arxiv","id":"2210.08529","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08529","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08529v2","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08529","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"pith_short_12","alias_value":"CXMPRZP6KAB2","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"pith_short_16","alias_value":"CXMPRZP6KAB2CIN6","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"pith_short_8","alias_value":"CXMPRZP6","created_at":"2026-07-05T05:44:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:CXMPRZP6KAB2CIN64UJLEY2BLL","target":"record","payload":{"canonical_record":{"source":{"id":"2210.08529","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-16T13:30:13Z","cross_cats_sorted":[],"title_canon_sha256":"782d925807524ca2bcaa7b40f4970a2abd6cbc1e79cac242bdd94b082ec6d705","abstract_canon_sha256":"bbdda82d75a8b7f18d663b875df9f586ca087eec89e597f64c897de2caded955"},"schema_version":"1.0"},"canonical_sha256":"15d8f8e5fe5003a121bee512b263415afa9fe9961959a673cc88fe139e7b8d00","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:39.007045Z","signature_b64":"whdedlb3P8YOMxqeeb81jSP0RCsEmRRjj+kX/5iLKsX63JcH0lYWFgnDb2y837dgD6TcP+1BchnmAuAf/Se2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15d8f8e5fe5003a121bee512b263415afa9fe9961959a673cc88fe139e7b8d00","last_reissued_at":"2026-07-05T05:44:39.006616Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:39.006616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.08529","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:44:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ao83yMKQM3BaB74X29OWJsS4QuZ4XfJJZBUGU8X8zBtMQDy7pDNPkAOwD8160Gw1Pj2sW5Nc19AMKDQ7SPjsCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T22:57:22.988136Z"},"content_sha256":"8a5b352e058904434fa074196927bb0a1275d7303844a207ea326b3fbf1de205","schema_version":"1.0","event_id":"sha256:8a5b352e058904434fa074196927bb0a1275d7303844a207ea326b3fbf1de205"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:CXMPRZP6KAB2CIN64UJLEY2BLL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Effective Image Manipulation Detection with Proposal Contrastive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bowen Zhao, Shanzhao Qiu, Shu-Tao Xia, Tao Dai, Yuyuan Zeng","submitted_at":"2022-10-16T13:30:13Z","abstract_excerpt":"Deep models have been widely and successfully used in image manipulation detection, which aims to classify tampered images and localize tampered regions. Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered image. To exploit such spatial relationships, we propose Proposal Contrastive Learning (PCL) for effective image manipulation detection. Our PCL consists of a two-stream architecture by extracting two types of global features from RGB and noi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08529","kind":"arxiv","version":2},"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/2210.08529/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:44:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6HcMtZ8rQdT0i6it2X+7l4aQO+tnUdE91XtJAd3E6ulytPkpZHl6H/dC18HoECPByGeggncm/HPUeBb9HNElCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T22:57:22.988520Z"},"content_sha256":"c7f85d10acf53dc8dbd63912940971310d93521f8a38dce01d2ef9a118db137e","schema_version":"1.0","event_id":"sha256:c7f85d10acf53dc8dbd63912940971310d93521f8a38dce01d2ef9a118db137e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CXMPRZP6KAB2CIN64UJLEY2BLL/bundle.json","state_url":"https://pith.science/pith/CXMPRZP6KAB2CIN64UJLEY2BLL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CXMPRZP6KAB2CIN64UJLEY2BLL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-08T22:57:22Z","links":{"resolver":"https://pith.science/pith/CXMPRZP6KAB2CIN64UJLEY2BLL","bundle":"https://pith.science/pith/CXMPRZP6KAB2CIN64UJLEY2BLL/bundle.json","state":"https://pith.science/pith/CXMPRZP6KAB2CIN64UJLEY2BLL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CXMPRZP6KAB2CIN64UJLEY2BLL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:CXMPRZP6KAB2CIN64UJLEY2BLL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"bbdda82d75a8b7f18d663b875df9f586ca087eec89e597f64c897de2caded955","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-16T13:30:13Z","title_canon_sha256":"782d925807524ca2bcaa7b40f4970a2abd6cbc1e79cac242bdd94b082ec6d705"},"schema_version":"1.0","source":{"id":"2210.08529","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.08529","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"arxiv_version","alias_value":"2210.08529v2","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.08529","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"pith_short_12","alias_value":"CXMPRZP6KAB2","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"pith_short_16","alias_value":"CXMPRZP6KAB2CIN6","created_at":"2026-07-05T05:44:39Z"},{"alias_kind":"pith_short_8","alias_value":"CXMPRZP6","created_at":"2026-07-05T05:44:39Z"}],"graph_snapshots":[{"event_id":"sha256:c7f85d10acf53dc8dbd63912940971310d93521f8a38dce01d2ef9a118db137e","target":"graph","created_at":"2026-07-05T05:44:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.08529/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep models have been widely and successfully used in image manipulation detection, which aims to classify tampered images and localize tampered regions. Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered image. To exploit such spatial relationships, we propose Proposal Contrastive Learning (PCL) for effective image manipulation detection. Our PCL consists of a two-stream architecture by extracting two types of global features from RGB and noi","authors_text":"Bowen Zhao, Shanzhao Qiu, Shu-Tao Xia, Tao Dai, Yuyuan Zeng","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-16T13:30:13Z","title":"Towards Effective Image Manipulation Detection with Proposal Contrastive Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.08529","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8a5b352e058904434fa074196927bb0a1275d7303844a207ea326b3fbf1de205","target":"record","created_at":"2026-07-05T05:44:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"bbdda82d75a8b7f18d663b875df9f586ca087eec89e597f64c897de2caded955","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-10-16T13:30:13Z","title_canon_sha256":"782d925807524ca2bcaa7b40f4970a2abd6cbc1e79cac242bdd94b082ec6d705"},"schema_version":"1.0","source":{"id":"2210.08529","kind":"arxiv","version":2}},"canonical_sha256":"15d8f8e5fe5003a121bee512b263415afa9fe9961959a673cc88fe139e7b8d00","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"15d8f8e5fe5003a121bee512b263415afa9fe9961959a673cc88fe139e7b8d00","first_computed_at":"2026-07-05T05:44:39.006616Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:44:39.006616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"whdedlb3P8YOMxqeeb81jSP0RCsEmRRjj+kX/5iLKsX63JcH0lYWFgnDb2y837dgD6TcP+1BchnmAuAf/Se2BA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:44:39.007045Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.08529","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8a5b352e058904434fa074196927bb0a1275d7303844a207ea326b3fbf1de205","sha256:c7f85d10acf53dc8dbd63912940971310d93521f8a38dce01d2ef9a118db137e"],"state_sha256":"cfff8885c460681809710043b2afaba24f9521ba2ae93309bb21e70836f65702"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bp5qPkw1uZ6ooRTWuk/O8pwysDoavjeK97oJlijS8R+S6ewZ458JIUCNPfGyjGhmNGWJs5PqhLb45NoXQeHgCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T22:57:22.990578Z","bundle_sha256":"45a03fe68a7f6e2cfce035b33f660406bdac336f2d54fa2f8f2e4adf320cc257"}}