{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:6OIP4PZCHM2U54GKEX7H3KH2WY","short_pith_number":"pith:6OIP4PZC","schema_version":"1.0","canonical_sha256":"f390fe3f223b354ef0ca25fe7da8fab61eefc2b9c77cd15256da0560f7061526","source":{"kind":"arxiv","id":"2201.04397","version":2},"attestation_state":"computed","paper":{"title":"Towards Adversarially Robust Deep Image Denoising","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Hanshu Yan, Jiashi Feng, Jingfeng Zhang, Masashi Sugiyama, Vincent Y. F. Tan","submitted_at":"2022-01-12T10:23:14Z","abstract_excerpt":"This work systematically investigates the adversarial robustness of deep image denoisers (DIDs), i.e, how well DIDs can recover the ground truth from noisy observations degraded by adversarial perturbations. Firstly, to evaluate DIDs' robustness, we propose a novel adversarial attack, namely Observation-based Zero-mean Attack ({\\sc ObsAtk}), to craft adversarial zero-mean perturbations on given noisy images. We find that existing DIDs are vulnerable to the adversarial noise generated by {\\sc ObsAtk}. Secondly, to robustify DIDs, we propose an adversarial training strategy, hybrid adversarial t"},"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":"2201.04397","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-01-12T10:23:14Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"0c8655bde234aceeed5c0c54a9f8d84e922dac6f9657fa8f5f05c2628788add5","abstract_canon_sha256":"9d6c49c5ae9588956cd4ffc45b2d2fb9a771063476add6418738f6f266cab3bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:48:11.257362Z","signature_b64":"s5YogeWdMkQFS9uLC3ds1KxvSB0JjxHKpjqxaa+2wrXlrzWH9oE98ABsaI3DahetzfuxrTPzsmXp9YvHgyZ2Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f390fe3f223b354ef0ca25fe7da8fab61eefc2b9c77cd15256da0560f7061526","last_reissued_at":"2026-07-05T03:48:11.256856Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:48:11.256856Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Adversarially Robust Deep Image Denoising","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Hanshu Yan, Jiashi Feng, Jingfeng Zhang, Masashi Sugiyama, Vincent Y. F. Tan","submitted_at":"2022-01-12T10:23:14Z","abstract_excerpt":"This work systematically investigates the adversarial robustness of deep image denoisers (DIDs), i.e, how well DIDs can recover the ground truth from noisy observations degraded by adversarial perturbations. Firstly, to evaluate DIDs' robustness, we propose a novel adversarial attack, namely Observation-based Zero-mean Attack ({\\sc ObsAtk}), to craft adversarial zero-mean perturbations on given noisy images. We find that existing DIDs are vulnerable to the adversarial noise generated by {\\sc ObsAtk}. Secondly, to robustify DIDs, we propose an adversarial training strategy, hybrid adversarial t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.04397","kind":"arxiv","version":2},"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/2201.04397/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":"2201.04397","created_at":"2026-07-05T03:48:11.256914+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.04397v2","created_at":"2026-07-05T03:48:11.256914+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.04397","created_at":"2026-07-05T03:48:11.256914+00:00"},{"alias_kind":"pith_short_12","alias_value":"6OIP4PZCHM2U","created_at":"2026-07-05T03:48:11.256914+00:00"},{"alias_kind":"pith_short_16","alias_value":"6OIP4PZCHM2U54GK","created_at":"2026-07-05T03:48:11.256914+00:00"},{"alias_kind":"pith_short_8","alias_value":"6OIP4PZC","created_at":"2026-07-05T03:48:11.256914+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.06024","citing_title":"On Inverse Problems, Parameter Estimation, and Domain Generalization","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY","json":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY.json","graph_json":"https://pith.science/api/pith-number/6OIP4PZCHM2U54GKEX7H3KH2WY/graph.json","events_json":"https://pith.science/api/pith-number/6OIP4PZCHM2U54GKEX7H3KH2WY/events.json","paper":"https://pith.science/paper/6OIP4PZC"},"agent_actions":{"view_html":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY","download_json":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY.json","view_paper":"https://pith.science/paper/6OIP4PZC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.04397&json=true","fetch_graph":"https://pith.science/api/pith-number/6OIP4PZCHM2U54GKEX7H3KH2WY/graph.json","fetch_events":"https://pith.science/api/pith-number/6OIP4PZCHM2U54GKEX7H3KH2WY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY/action/storage_attestation","attest_author":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY/action/author_attestation","sign_citation":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY/action/citation_signature","submit_replication":"https://pith.science/pith/6OIP4PZCHM2U54GKEX7H3KH2WY/action/replication_record"}},"created_at":"2026-07-05T03:48:11.256914+00:00","updated_at":"2026-07-05T03:48:11.256914+00:00"}