{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VKX2VG5XGB6CK2HKMETIVV57AH","short_pith_number":"pith:VKX2VG5X","schema_version":"1.0","canonical_sha256":"aaafaa9bb7307c2568ea61268ad7bf01c2a93e4257e5346a9bdd25bbaf850da1","source":{"kind":"arxiv","id":"2205.15867","version":1},"attestation_state":"computed","paper":{"title":"Median Pixel Difference Convolutional Network for Robust Face Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Jiehua Zhang, Li Liu, Zhuo Su","submitted_at":"2022-05-30T13:15:49Z","abstract_excerpt":"Face recognition is one of the most active tasks in computer vision and has been widely used in the real world. With great advances made in convolutional neural networks (CNN), lots of face recognition algorithms have achieved high accuracy on various face datasets. However, existing face recognition algorithms based on CNNs are vulnerable to noise. Noise corrupted image patterns could lead to false activations, significantly decreasing face recognition accuracy in noisy situations. To equip CNNs with built-in robustness to noise of different levels, we proposed a Median Pixel Difference Convo"},"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":"2205.15867","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-05-30T13:15:49Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"4765790d01d7b9ac03778d0951e83daeeae045836a18501f6067acd12fa5bf65","abstract_canon_sha256":"0ced376cea1be023bc641dd7a30061290520e0c40623cb81b52ccc70cbb3119d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:28:03.158167Z","signature_b64":"PTmm4u9Y2Ni1tMOn4VGcsQ5CMCOy1AZPFM//4Wet66jc+h4dDfJtjbMe86Je+eUVY4FwmSXrY9De1mFR8Oz6Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaafaa9bb7307c2568ea61268ad7bf01c2a93e4257e5346a9bdd25bbaf850da1","last_reissued_at":"2026-07-05T04:28:03.157748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:28:03.157748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Median Pixel Difference Convolutional Network for Robust Face Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Jiehua Zhang, Li Liu, Zhuo Su","submitted_at":"2022-05-30T13:15:49Z","abstract_excerpt":"Face recognition is one of the most active tasks in computer vision and has been widely used in the real world. With great advances made in convolutional neural networks (CNN), lots of face recognition algorithms have achieved high accuracy on various face datasets. However, existing face recognition algorithms based on CNNs are vulnerable to noise. Noise corrupted image patterns could lead to false activations, significantly decreasing face recognition accuracy in noisy situations. To equip CNNs with built-in robustness to noise of different levels, we proposed a Median Pixel Difference Convo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.15867","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/2205.15867/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":"2205.15867","created_at":"2026-07-05T04:28:03.157807+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.15867v1","created_at":"2026-07-05T04:28:03.157807+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.15867","created_at":"2026-07-05T04:28:03.157807+00:00"},{"alias_kind":"pith_short_12","alias_value":"VKX2VG5XGB6C","created_at":"2026-07-05T04:28:03.157807+00:00"},{"alias_kind":"pith_short_16","alias_value":"VKX2VG5XGB6CK2HK","created_at":"2026-07-05T04:28:03.157807+00:00"},{"alias_kind":"pith_short_8","alias_value":"VKX2VG5X","created_at":"2026-07-05T04:28:03.157807+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/VKX2VG5XGB6CK2HKMETIVV57AH","json":"https://pith.science/pith/VKX2VG5XGB6CK2HKMETIVV57AH.json","graph_json":"https://pith.science/api/pith-number/VKX2VG5XGB6CK2HKMETIVV57AH/graph.json","events_json":"https://pith.science/api/pith-number/VKX2VG5XGB6CK2HKMETIVV57AH/events.json","paper":"https://pith.science/paper/VKX2VG5X"},"agent_actions":{"view_html":"https://pith.science/pith/VKX2VG5XGB6CK2HKMETIVV57AH","download_json":"https://pith.science/pith/VKX2VG5XGB6CK2HKMETIVV57AH.json","view_paper":"https://pith.science/paper/VKX2VG5X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.15867&json=true","fetch_graph":"https://pith.science/api/pith-number/VKX2VG5XGB6CK2HKMETIVV57AH/graph.json","fetch_events":"https://pith.science/api/pith-number/VKX2VG5XGB6CK2HKMETIVV57AH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VKX2VG5XGB6CK2HKMETIVV57AH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VKX2VG5XGB6CK2HKMETIVV57AH/action/storage_attestation","attest_author":"https://pith.science/pith/VKX2VG5XGB6CK2HKMETIVV57AH/action/author_attestation","sign_citation":"https://pith.science/pith/VKX2VG5XGB6CK2HKMETIVV57AH/action/citation_signature","submit_replication":"https://pith.science/pith/VKX2VG5XGB6CK2HKMETIVV57AH/action/replication_record"}},"created_at":"2026-07-05T04:28:03.157807+00:00","updated_at":"2026-07-05T04:28:03.157807+00:00"}