{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:IL5ZJS4TPBA7QIYJYKHPC6GHL7","short_pith_number":"pith:IL5ZJS4T","schema_version":"1.0","canonical_sha256":"42fb94cb937841f82309c28ef178c75fd8175f390e2979fec19495e6c5b2d6ec","source":{"kind":"arxiv","id":"2203.16780","version":1},"attestation_state":"computed","paper":{"title":"A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CR","authors_text":"Ijaz Ahmad, Seokjoo Shin","submitted_at":"2022-03-31T03:42:11Z","abstract_excerpt":"In the recent years, pixel-based perceptual algorithms have been successfully applied for privacy-preserving deep learning (DL) based applications. However, their security has been broken in subsequent works by demonstrating a chosen-plaintext attack. In this paper, we propose an efficient pixel-based perceptual encryption method. The method provides a necessary level of security while preserving the intrinsic properties of the original image. Thereby, can enable deep learning (DL) applications in the encryption domain. The method is substitution based where pixel values are XORed with a seque"},"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":"2203.16780","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2022-03-31T03:42:11Z","cross_cats_sorted":["cs.AI","cs.MM"],"title_canon_sha256":"4d4c83d713475e30080c03d968e8aeefe4b8358f1f21b3dc2346600f9dd4eaf8","abstract_canon_sha256":"680b0fa10f3b9944fe63c5ba76bd7aaafeb965d31f27be6ba85850d659fbb281"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:12:18.483737Z","signature_b64":"xx8RYWRS2jgH0SIBna3Z9FGO1lKpUdUfuHAFIdGoHO7K/89EiWXT8BRQMcrC9UaTYL/ZrHvBvckB+gdHM/eJDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42fb94cb937841f82309c28ef178c75fd8175f390e2979fec19495e6c5b2d6ec","last_reissued_at":"2026-07-05T04:12:18.483332Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:12:18.483332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MM"],"primary_cat":"cs.CR","authors_text":"Ijaz Ahmad, Seokjoo Shin","submitted_at":"2022-03-31T03:42:11Z","abstract_excerpt":"In the recent years, pixel-based perceptual algorithms have been successfully applied for privacy-preserving deep learning (DL) based applications. However, their security has been broken in subsequent works by demonstrating a chosen-plaintext attack. In this paper, we propose an efficient pixel-based perceptual encryption method. The method provides a necessary level of security while preserving the intrinsic properties of the original image. Thereby, can enable deep learning (DL) applications in the encryption domain. The method is substitution based where pixel values are XORed with a seque"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.16780","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/2203.16780/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":"2203.16780","created_at":"2026-07-05T04:12:18.483388+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.16780v1","created_at":"2026-07-05T04:12:18.483388+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.16780","created_at":"2026-07-05T04:12:18.483388+00:00"},{"alias_kind":"pith_short_12","alias_value":"IL5ZJS4TPBA7","created_at":"2026-07-05T04:12:18.483388+00:00"},{"alias_kind":"pith_short_16","alias_value":"IL5ZJS4TPBA7QIYJ","created_at":"2026-07-05T04:12:18.483388+00:00"},{"alias_kind":"pith_short_8","alias_value":"IL5ZJS4T","created_at":"2026-07-05T04:12:18.483388+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/IL5ZJS4TPBA7QIYJYKHPC6GHL7","json":"https://pith.science/pith/IL5ZJS4TPBA7QIYJYKHPC6GHL7.json","graph_json":"https://pith.science/api/pith-number/IL5ZJS4TPBA7QIYJYKHPC6GHL7/graph.json","events_json":"https://pith.science/api/pith-number/IL5ZJS4TPBA7QIYJYKHPC6GHL7/events.json","paper":"https://pith.science/paper/IL5ZJS4T"},"agent_actions":{"view_html":"https://pith.science/pith/IL5ZJS4TPBA7QIYJYKHPC6GHL7","download_json":"https://pith.science/pith/IL5ZJS4TPBA7QIYJYKHPC6GHL7.json","view_paper":"https://pith.science/paper/IL5ZJS4T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.16780&json=true","fetch_graph":"https://pith.science/api/pith-number/IL5ZJS4TPBA7QIYJYKHPC6GHL7/graph.json","fetch_events":"https://pith.science/api/pith-number/IL5ZJS4TPBA7QIYJYKHPC6GHL7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IL5ZJS4TPBA7QIYJYKHPC6GHL7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IL5ZJS4TPBA7QIYJYKHPC6GHL7/action/storage_attestation","attest_author":"https://pith.science/pith/IL5ZJS4TPBA7QIYJYKHPC6GHL7/action/author_attestation","sign_citation":"https://pith.science/pith/IL5ZJS4TPBA7QIYJYKHPC6GHL7/action/citation_signature","submit_replication":"https://pith.science/pith/IL5ZJS4TPBA7QIYJYKHPC6GHL7/action/replication_record"}},"created_at":"2026-07-05T04:12:18.483388+00:00","updated_at":"2026-07-05T04:12:18.483388+00:00"}