{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:FY2UK6BTQVFIZEI4EEOWJOUG5Z","short_pith_number":"pith:FY2UK6BT","schema_version":"1.0","canonical_sha256":"2e35457833854a8c911c211d64ba86ee55be6381762da875efd66cf576de31da","source":{"kind":"arxiv","id":"1802.04881","version":1},"attestation_state":"computed","paper":{"title":"Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David G\\\"uera, Edward J. Delp, Fengqing Maggie Zhu, Paolo Bestagini, Sri Kalyan Yarlagadda, Stefano Tubaro","submitted_at":"2018-02-13T22:28:58Z","abstract_excerpt":"Current satellite imaging technology enables shooting high-resolution pictures of the ground. As any other kind of digital images, overhead pictures can also be easily forged. However, common image forensic techniques are often developed for consumer camera images, which strongly differ in their nature from satellite ones (e.g., compression schemes, post-processing, sensors, etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound to fail if blindly applied to overhead image analysis. Development of novel forensic tools for satellite images is paramount to assess their au"},"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":"1802.04881","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-13T22:28:58Z","cross_cats_sorted":[],"title_canon_sha256":"a6fe1c531e1e3916f02f152f5186f7d50b386fd6388ffccb91fac7c37e10a353","abstract_canon_sha256":"e3b986a4aba61409b3057aded18dee8185f64d409a0a26514c3c2accd5a9f82d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:22.355709Z","signature_b64":"U+HJlCk4Z3jwItwDjz4xc5BKbzzIiZzj8lD9RUK/CkK8pmTjFttRpzwvGQ7TClfCLTbN1jvVnM6YKvNkbUTnDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e35457833854a8c911c211d64ba86ee55be6381762da875efd66cf576de31da","last_reissued_at":"2026-05-18T00:23:22.354990Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:22.354990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"David G\\\"uera, Edward J. Delp, Fengqing Maggie Zhu, Paolo Bestagini, Sri Kalyan Yarlagadda, Stefano Tubaro","submitted_at":"2018-02-13T22:28:58Z","abstract_excerpt":"Current satellite imaging technology enables shooting high-resolution pictures of the ground. As any other kind of digital images, overhead pictures can also be easily forged. However, common image forensic techniques are often developed for consumer camera images, which strongly differ in their nature from satellite ones (e.g., compression schemes, post-processing, sensors, etc.). Therefore, many accurate state-of-the-art forensic algorithms are bound to fail if blindly applied to overhead image analysis. Development of novel forensic tools for satellite images is paramount to assess their au"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.04881","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":"1802.04881","created_at":"2026-05-18T00:23:22.355086+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.04881v1","created_at":"2026-05-18T00:23:22.355086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.04881","created_at":"2026-05-18T00:23:22.355086+00:00"},{"alias_kind":"pith_short_12","alias_value":"FY2UK6BTQVFI","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"FY2UK6BTQVFIZEI4","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"FY2UK6BT","created_at":"2026-05-18T12:32:25.280505+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/FY2UK6BTQVFIZEI4EEOWJOUG5Z","json":"https://pith.science/pith/FY2UK6BTQVFIZEI4EEOWJOUG5Z.json","graph_json":"https://pith.science/api/pith-number/FY2UK6BTQVFIZEI4EEOWJOUG5Z/graph.json","events_json":"https://pith.science/api/pith-number/FY2UK6BTQVFIZEI4EEOWJOUG5Z/events.json","paper":"https://pith.science/paper/FY2UK6BT"},"agent_actions":{"view_html":"https://pith.science/pith/FY2UK6BTQVFIZEI4EEOWJOUG5Z","download_json":"https://pith.science/pith/FY2UK6BTQVFIZEI4EEOWJOUG5Z.json","view_paper":"https://pith.science/paper/FY2UK6BT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.04881&json=true","fetch_graph":"https://pith.science/api/pith-number/FY2UK6BTQVFIZEI4EEOWJOUG5Z/graph.json","fetch_events":"https://pith.science/api/pith-number/FY2UK6BTQVFIZEI4EEOWJOUG5Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FY2UK6BTQVFIZEI4EEOWJOUG5Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FY2UK6BTQVFIZEI4EEOWJOUG5Z/action/storage_attestation","attest_author":"https://pith.science/pith/FY2UK6BTQVFIZEI4EEOWJOUG5Z/action/author_attestation","sign_citation":"https://pith.science/pith/FY2UK6BTQVFIZEI4EEOWJOUG5Z/action/citation_signature","submit_replication":"https://pith.science/pith/FY2UK6BTQVFIZEI4EEOWJOUG5Z/action/replication_record"}},"created_at":"2026-05-18T00:23:22.355086+00:00","updated_at":"2026-05-18T00:23:22.355086+00:00"}