{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:53K5BUPDYMUQ6FDHVSWFCSLDGI","short_pith_number":"pith:53K5BUPD","canonical_record":{"source":{"id":"2412.11214","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-15T15:10:53Z","cross_cats_sorted":[],"title_canon_sha256":"c933055968e3d73b8573cff46ccaf6a41d96b78c1aa3c7bd50060bd2e84976a9","abstract_canon_sha256":"21a80816e6249e05478225ac087eec6e6f8558ddf9e73fa4e9c572646a6cfd0a"},"schema_version":"1.0"},"canonical_sha256":"eed5d0d1e3c3290f1467acac51496332251b89d11a84d6e19b24be0e29d42eda","source":{"kind":"arxiv","id":"2412.11214","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.11214","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"arxiv_version","alias_value":"2412.11214v2","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11214","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_12","alias_value":"53K5BUPDYMUQ","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_16","alias_value":"53K5BUPDYMUQ6FDH","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_8","alias_value":"53K5BUPD","created_at":"2026-07-05T10:14:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:53K5BUPDYMUQ6FDHVSWFCSLDGI","target":"record","payload":{"canonical_record":{"source":{"id":"2412.11214","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-15T15:10:53Z","cross_cats_sorted":[],"title_canon_sha256":"c933055968e3d73b8573cff46ccaf6a41d96b78c1aa3c7bd50060bd2e84976a9","abstract_canon_sha256":"21a80816e6249e05478225ac087eec6e6f8558ddf9e73fa4e9c572646a6cfd0a"},"schema_version":"1.0"},"canonical_sha256":"eed5d0d1e3c3290f1467acac51496332251b89d11a84d6e19b24be0e29d42eda","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:14:07.698811Z","signature_b64":"XT1G6CgG0LiSAzFg/Cs/M28UtlQbsBlN4gxTEwFuOrpzoMo7hJiFEXMxOmuNGivbSwlNz7pFaSTcyGJciJdaDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eed5d0d1e3c3290f1467acac51496332251b89d11a84d6e19b24be0e29d42eda","last_reissued_at":"2026-07-05T10:14:07.698307Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:14:07.698307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.11214","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":"2026-07-05T10:14:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R2h1GrsDZkrtpXXyri1ADtWdqC++cYqOejicVfpNVJ1K+Fr4H9AWFjYUWqy6KyvnyUErLfZd+6aX5K96ORf7DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T00:13:01.806746Z"},"content_sha256":"5fa7ea4eba459346e93d90e8b88b3b093b142d290545b5a46ebf500e5b6a9ffa","schema_version":"1.0","event_id":"sha256:5fa7ea4eba459346e93d90e8b88b3b093b142d290545b5a46ebf500e5b6a9ffa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:53K5BUPDYMUQ6FDHVSWFCSLDGI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Image Forgery Localization with State Space Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gang Cao, Kun Guo, Lifang Yu, Shaowei Weng, Zijie Lou","submitted_at":"2024-12-15T15:10:53Z","abstract_excerpt":"Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, we propose LoMa, a novel image forgery localization method that leverages the selec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11214","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/2412.11214/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":"2026-07-05T10:14:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d5l3hIAOXBq2TG+FWkNi5Np+hm6LRD0aVMCnSXPBskmn164hKp54nDTSF3TXsiw+F+sj7fsvaFwrb747f6K+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T00:13:01.807132Z"},"content_sha256":"b2bcae11fa1e5da13d033fd2aa12862e393e8ec4f2cb17c1d34d8b130103570a","schema_version":"1.0","event_id":"sha256:b2bcae11fa1e5da13d033fd2aa12862e393e8ec4f2cb17c1d34d8b130103570a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/53K5BUPDYMUQ6FDHVSWFCSLDGI/bundle.json","state_url":"https://pith.science/pith/53K5BUPDYMUQ6FDHVSWFCSLDGI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/53K5BUPDYMUQ6FDHVSWFCSLDGI/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":"2026-07-09T00:13:01Z","links":{"resolver":"https://pith.science/pith/53K5BUPDYMUQ6FDHVSWFCSLDGI","bundle":"https://pith.science/pith/53K5BUPDYMUQ6FDHVSWFCSLDGI/bundle.json","state":"https://pith.science/pith/53K5BUPDYMUQ6FDHVSWFCSLDGI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/53K5BUPDYMUQ6FDHVSWFCSLDGI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:53K5BUPDYMUQ6FDHVSWFCSLDGI","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":"21a80816e6249e05478225ac087eec6e6f8558ddf9e73fa4e9c572646a6cfd0a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-15T15:10:53Z","title_canon_sha256":"c933055968e3d73b8573cff46ccaf6a41d96b78c1aa3c7bd50060bd2e84976a9"},"schema_version":"1.0","source":{"id":"2412.11214","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.11214","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"arxiv_version","alias_value":"2412.11214v2","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11214","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_12","alias_value":"53K5BUPDYMUQ","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_16","alias_value":"53K5BUPDYMUQ6FDH","created_at":"2026-07-05T10:14:07Z"},{"alias_kind":"pith_short_8","alias_value":"53K5BUPD","created_at":"2026-07-05T10:14:07Z"}],"graph_snapshots":[{"event_id":"sha256:b2bcae11fa1e5da13d033fd2aa12862e393e8ec4f2cb17c1d34d8b130103570a","target":"graph","created_at":"2026-07-05T10:14:07Z","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/2412.11214/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pixel dependency modeling from tampered images is pivotal for image forgery localization. Current approaches predominantly rely on Convolutional Neural Networks (CNNs) or Transformer-based models, which often either lack sufficient receptive fields or entail significant computational overheads. Recently, State Space Models (SSMs), exemplified by Mamba, have emerged as a promising approach. They not only excel in modeling long-range interactions but also maintain a linear computational complexity. In this paper, we propose LoMa, a novel image forgery localization method that leverages the selec","authors_text":"Gang Cao, Kun Guo, Lifang Yu, Shaowei Weng, Zijie Lou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-15T15:10:53Z","title":"Image Forgery Localization with State Space Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11214","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":"sha256:5fa7ea4eba459346e93d90e8b88b3b093b142d290545b5a46ebf500e5b6a9ffa","target":"record","created_at":"2026-07-05T10:14:07Z","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":"21a80816e6249e05478225ac087eec6e6f8558ddf9e73fa4e9c572646a6cfd0a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-15T15:10:53Z","title_canon_sha256":"c933055968e3d73b8573cff46ccaf6a41d96b78c1aa3c7bd50060bd2e84976a9"},"schema_version":"1.0","source":{"id":"2412.11214","kind":"arxiv","version":2}},"canonical_sha256":"eed5d0d1e3c3290f1467acac51496332251b89d11a84d6e19b24be0e29d42eda","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eed5d0d1e3c3290f1467acac51496332251b89d11a84d6e19b24be0e29d42eda","first_computed_at":"2026-07-05T10:14:07.698307Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:14:07.698307Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XT1G6CgG0LiSAzFg/Cs/M28UtlQbsBlN4gxTEwFuOrpzoMo7hJiFEXMxOmuNGivbSwlNz7pFaSTcyGJciJdaDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:14:07.698811Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.11214","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5fa7ea4eba459346e93d90e8b88b3b093b142d290545b5a46ebf500e5b6a9ffa","sha256:b2bcae11fa1e5da13d033fd2aa12862e393e8ec4f2cb17c1d34d8b130103570a"],"state_sha256":"8c642c0ed2d73077d0b57e1aece1f44ab3b17605d01f2c0f201ca819f2517433"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jpRr5/GeeP3ihylHlIMX1aUfVcIhPX7HP0vQCls8DIBIoDWA2cLh+4258F8oWgpzsJZlkAnNPceEgE5V0nfIDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T00:13:01.809443Z","bundle_sha256":"c69fa7cf6c2882e0de722f67d9ecfa6854c1354954f18a01a564062b126cb79e"}}