{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:XAEHTDYDMIF3G3IPRSQFC32XU5","short_pith_number":"pith:XAEHTDYD","schema_version":"1.0","canonical_sha256":"b808798f03620bb36d0f8ca0516f57a748f5f1a938064d7acae782d0574fb9b6","source":{"kind":"arxiv","id":"2208.02482","version":1},"attestation_state":"computed","paper":{"title":"Privacy Safe Representation Learning via Frequency Filtering Encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jeewook Kim, Jinwoo Hwang, Jonghu Jeong, Minyong Cho, Philipp Benz, Seungkwan Lee, Tae-Hoon Kim","submitted_at":"2022-08-04T06:16:13Z","abstract_excerpt":"Deep learning models are increasingly deployed in real-world applications. These models are often deployed on the server-side and receive user data in an information-rich representation to solve a specific task, such as image classification. Since images can contain sensitive information, which users might not be willing to share, privacy protection becomes increasingly important. Adversarial Representation Learning (ARL) is a common approach to train an encoder that runs on the client-side and obfuscates an image. It is assumed, that the obfuscated image can safely be transmitted and used for"},"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":"2208.02482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-08-04T06:16:13Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"41cff4841b6052e4b47fb93c46d3bb34eb067214271248681bb60aec5d2e5868","abstract_canon_sha256":"e4a71a932052e664cae4666f73b4c3324a0fb850cd84f8304cc4f27bfc8bb939"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:46:04.969263Z","signature_b64":"pEc2Go71ChXyR/DXLc0mO6oZp/GJccRva2rY2rjKBHk2QHOBYIsIQszHmWfXzgDjQxbwKFkevr60gpsENMqOBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b808798f03620bb36d0f8ca0516f57a748f5f1a938064d7acae782d0574fb9b6","last_reissued_at":"2026-07-05T04:46:04.968855Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:46:04.968855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Privacy Safe Representation Learning via Frequency Filtering Encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jeewook Kim, Jinwoo Hwang, Jonghu Jeong, Minyong Cho, Philipp Benz, Seungkwan Lee, Tae-Hoon Kim","submitted_at":"2022-08-04T06:16:13Z","abstract_excerpt":"Deep learning models are increasingly deployed in real-world applications. These models are often deployed on the server-side and receive user data in an information-rich representation to solve a specific task, such as image classification. Since images can contain sensitive information, which users might not be willing to share, privacy protection becomes increasingly important. Adversarial Representation Learning (ARL) is a common approach to train an encoder that runs on the client-side and obfuscates an image. It is assumed, that the obfuscated image can safely be transmitted and used for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.02482","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/2208.02482/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":"2208.02482","created_at":"2026-07-05T04:46:04.968909+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.02482v1","created_at":"2026-07-05T04:46:04.968909+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.02482","created_at":"2026-07-05T04:46:04.968909+00:00"},{"alias_kind":"pith_short_12","alias_value":"XAEHTDYDMIF3","created_at":"2026-07-05T04:46:04.968909+00:00"},{"alias_kind":"pith_short_16","alias_value":"XAEHTDYDMIF3G3IP","created_at":"2026-07-05T04:46:04.968909+00:00"},{"alias_kind":"pith_short_8","alias_value":"XAEHTDYD","created_at":"2026-07-05T04:46:04.968909+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/XAEHTDYDMIF3G3IPRSQFC32XU5","json":"https://pith.science/pith/XAEHTDYDMIF3G3IPRSQFC32XU5.json","graph_json":"https://pith.science/api/pith-number/XAEHTDYDMIF3G3IPRSQFC32XU5/graph.json","events_json":"https://pith.science/api/pith-number/XAEHTDYDMIF3G3IPRSQFC32XU5/events.json","paper":"https://pith.science/paper/XAEHTDYD"},"agent_actions":{"view_html":"https://pith.science/pith/XAEHTDYDMIF3G3IPRSQFC32XU5","download_json":"https://pith.science/pith/XAEHTDYDMIF3G3IPRSQFC32XU5.json","view_paper":"https://pith.science/paper/XAEHTDYD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.02482&json=true","fetch_graph":"https://pith.science/api/pith-number/XAEHTDYDMIF3G3IPRSQFC32XU5/graph.json","fetch_events":"https://pith.science/api/pith-number/XAEHTDYDMIF3G3IPRSQFC32XU5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XAEHTDYDMIF3G3IPRSQFC32XU5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XAEHTDYDMIF3G3IPRSQFC32XU5/action/storage_attestation","attest_author":"https://pith.science/pith/XAEHTDYDMIF3G3IPRSQFC32XU5/action/author_attestation","sign_citation":"https://pith.science/pith/XAEHTDYDMIF3G3IPRSQFC32XU5/action/citation_signature","submit_replication":"https://pith.science/pith/XAEHTDYDMIF3G3IPRSQFC32XU5/action/replication_record"}},"created_at":"2026-07-05T04:46:04.968909+00:00","updated_at":"2026-07-05T04:46:04.968909+00:00"}