{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:FR5LOWMVDBTY4DUNUCUGBOOW2C","short_pith_number":"pith:FR5LOWMV","schema_version":"1.0","canonical_sha256":"2c7ab7599518678e0e8da0a860b9d6d08a8a0dc3b28bf0e819d236171c82f365","source":{"kind":"arxiv","id":"1707.00433","version":1},"attestation_state":"computed","paper":{"title":"Detection and Localization of Image Forgeries using Resampling Features and Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amit K. Roy-Chowdhury, Arjuna Flenner, B.S. Manjunath, Jason Bunk, Jawadul H. Bappy, Lakshmanan Nataraj, Lawrence Peterson, Shivkumar Chandrasekaran, Tajuddin Manhar Mohammed","submitted_at":"2017-07-03T07:50:15Z","abstract_excerpt":"Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long sh"},"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":"1707.00433","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-03T07:50:15Z","cross_cats_sorted":[],"title_canon_sha256":"12a3ab99380bd1a7d7d344a860834942fe90219bfa3e70a61200efebbdd6ece3","abstract_canon_sha256":"2ecbfbad46ce8afaeccd3e318dbaa1e6a331cdde2f1b523e3f2d50a72c869750"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:03.394231Z","signature_b64":"4T7/diqW14cpoADvggNKpRhbm6j/a1Xr/EiWb+Yb983IjJMbb8kZnjp1nF8M48OkYb8z6QzGu52pkspe3RnIDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c7ab7599518678e0e8da0a860b9d6d08a8a0dc3b28bf0e819d236171c82f365","last_reissued_at":"2026-05-18T00:41:03.393652Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:03.393652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detection and Localization of Image Forgeries using Resampling Features and Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amit K. Roy-Chowdhury, Arjuna Flenner, B.S. Manjunath, Jason Bunk, Jawadul H. Bappy, Lakshmanan Nataraj, Lawrence Peterson, Shivkumar Chandrasekaran, Tajuddin Manhar Mohammed","submitted_at":"2017-07-03T07:50:15Z","abstract_excerpt":"Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long sh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.00433","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":""},"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":"1707.00433","created_at":"2026-05-18T00:41:03.393747+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.00433v1","created_at":"2026-05-18T00:41:03.393747+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.00433","created_at":"2026-05-18T00:41:03.393747+00:00"},{"alias_kind":"pith_short_12","alias_value":"FR5LOWMVDBTY","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_16","alias_value":"FR5LOWMVDBTY4DUN","created_at":"2026-05-18T12:31:15.632608+00:00"},{"alias_kind":"pith_short_8","alias_value":"FR5LOWMV","created_at":"2026-05-18T12:31:15.632608+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C","json":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C.json","graph_json":"https://pith.science/api/pith-number/FR5LOWMVDBTY4DUNUCUGBOOW2C/graph.json","events_json":"https://pith.science/api/pith-number/FR5LOWMVDBTY4DUNUCUGBOOW2C/events.json","paper":"https://pith.science/paper/FR5LOWMV"},"agent_actions":{"view_html":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C","download_json":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C.json","view_paper":"https://pith.science/paper/FR5LOWMV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.00433&json=true","fetch_graph":"https://pith.science/api/pith-number/FR5LOWMVDBTY4DUNUCUGBOOW2C/graph.json","fetch_events":"https://pith.science/api/pith-number/FR5LOWMVDBTY4DUNUCUGBOOW2C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C/action/storage_attestation","attest_author":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C/action/author_attestation","sign_citation":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C/action/citation_signature","submit_replication":"https://pith.science/pith/FR5LOWMVDBTY4DUNUCUGBOOW2C/action/replication_record"}},"created_at":"2026-05-18T00:41:03.393747+00:00","updated_at":"2026-05-18T00:41:03.393747+00:00"}