{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:I25PXWAQ6JEYS54F5VMI33SDOW","short_pith_number":"pith:I25PXWAQ","schema_version":"1.0","canonical_sha256":"46bafbd810f249897785ed588dee43759b13ef46bfc633075ca944f45803935f","source":{"kind":"arxiv","id":"2303.13846","version":1},"attestation_state":"computed","paper":{"title":"Feature Separation and Recalibration for Adversarial Robustness","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junsik Jung, Sung-Eui Yoon, Woo Jae Kim, Yoonki Cho","submitted_at":"2023-03-24T07:43:57Z","abstract_excerpt":"Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions. However, we claim that these malicious activations still contain discriminative cues and that with recalibration, they can capture additional useful information for correct model predictions. To this end, we propose a novel, easy-to-plugin approach named Feature Separation and Recalibration (FSR) that recalibrates the malicious, non-robust ac"},"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":"2303.13846","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-24T07:43:57Z","cross_cats_sorted":[],"title_canon_sha256":"15e9d722e1ee3a669e12220d4341bc4371b1809d287e878eeb47b02e9abbab1b","abstract_canon_sha256":"f45234e3f0e8b526dc248c1ee5d992ec1b1c68faa5289be2bf886935e7e830ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:54:20.725977Z","signature_b64":"tqhs6aR0WhHuuk8AWllDitTBSwu54YTE/F6C0wr+/bfjpDAYw2CdnrAHg6oWrrzqGxqKXWi5WfZnfnGhE3LWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46bafbd810f249897785ed588dee43759b13ef46bfc633075ca944f45803935f","last_reissued_at":"2026-07-05T05:54:20.725484Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:54:20.725484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feature Separation and Recalibration for Adversarial Robustness","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Junsik Jung, Sung-Eui Yoon, Woo Jae Kim, Yoonki Cho","submitted_at":"2023-03-24T07:43:57Z","abstract_excerpt":"Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions. However, we claim that these malicious activations still contain discriminative cues and that with recalibration, they can capture additional useful information for correct model predictions. To this end, we propose a novel, easy-to-plugin approach named Feature Separation and Recalibration (FSR) that recalibrates the malicious, non-robust ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.13846","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/2303.13846/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":"2303.13846","created_at":"2026-07-05T05:54:20.725543+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.13846v1","created_at":"2026-07-05T05:54:20.725543+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.13846","created_at":"2026-07-05T05:54:20.725543+00:00"},{"alias_kind":"pith_short_12","alias_value":"I25PXWAQ6JEY","created_at":"2026-07-05T05:54:20.725543+00:00"},{"alias_kind":"pith_short_16","alias_value":"I25PXWAQ6JEYS54F","created_at":"2026-07-05T05:54:20.725543+00:00"},{"alias_kind":"pith_short_8","alias_value":"I25PXWAQ","created_at":"2026-07-05T05:54:20.725543+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/I25PXWAQ6JEYS54F5VMI33SDOW","json":"https://pith.science/pith/I25PXWAQ6JEYS54F5VMI33SDOW.json","graph_json":"https://pith.science/api/pith-number/I25PXWAQ6JEYS54F5VMI33SDOW/graph.json","events_json":"https://pith.science/api/pith-number/I25PXWAQ6JEYS54F5VMI33SDOW/events.json","paper":"https://pith.science/paper/I25PXWAQ"},"agent_actions":{"view_html":"https://pith.science/pith/I25PXWAQ6JEYS54F5VMI33SDOW","download_json":"https://pith.science/pith/I25PXWAQ6JEYS54F5VMI33SDOW.json","view_paper":"https://pith.science/paper/I25PXWAQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.13846&json=true","fetch_graph":"https://pith.science/api/pith-number/I25PXWAQ6JEYS54F5VMI33SDOW/graph.json","fetch_events":"https://pith.science/api/pith-number/I25PXWAQ6JEYS54F5VMI33SDOW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I25PXWAQ6JEYS54F5VMI33SDOW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I25PXWAQ6JEYS54F5VMI33SDOW/action/storage_attestation","attest_author":"https://pith.science/pith/I25PXWAQ6JEYS54F5VMI33SDOW/action/author_attestation","sign_citation":"https://pith.science/pith/I25PXWAQ6JEYS54F5VMI33SDOW/action/citation_signature","submit_replication":"https://pith.science/pith/I25PXWAQ6JEYS54F5VMI33SDOW/action/replication_record"}},"created_at":"2026-07-05T05:54:20.725543+00:00","updated_at":"2026-07-05T05:54:20.725543+00:00"}