{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:G6Q6STL36INROCETRQWFIBGPD5","short_pith_number":"pith:G6Q6STL3","schema_version":"1.0","canonical_sha256":"37a1e94d7bf21b1708938c2c5404cf1f46d5604d85a65f2044c4bac94d0d26d2","source":{"kind":"arxiv","id":"2412.11119","version":1},"attestation_state":"computed","paper":{"title":"Impact of Adversarial Attacks on Deep Learning Model Explainability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Gazi Nazia Nur, Mohammad Ahnaf Sadat","submitted_at":"2024-12-15T08:41:37Z","abstract_excerpt":"In this paper, we investigate the impact of adversarial attacks on the explainability of deep learning models, which are commonly criticized for their black-box nature despite their capacity for autonomous feature extraction. This black-box nature can affect the perceived trustworthiness of these models. To address this, explainability techniques such as GradCAM, SmoothGrad, and LIME have been developed to clarify model decision-making processes. Our research focuses on the robustness of these explanations when models are subjected to adversarial attacks, specifically those involving subtle im"},"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":"2412.11119","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-15T08:41:37Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"db5f66e61b6a56840d03ee8141a0ee8394f5f5bf6a12163b0059df451db8ad57","abstract_canon_sha256":"94d3a1ff532791d537e2a16f78b18ab8dd36b8f505786cd1f8146508ce232a82"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:27.812466Z","signature_b64":"d0YSjs1zr6jRp4q2nkDdh/RiQLTI5Lx2jsutN36z5Jya0mXb23upl0jqGi3p5S9Dg1/wPpzJXHKF2Yp8IENdAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37a1e94d7bf21b1708938c2c5404cf1f46d5604d85a65f2044c4bac94d0d26d2","last_reissued_at":"2026-07-05T09:49:27.812054Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:27.812054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Impact of Adversarial Attacks on Deep Learning Model Explainability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Gazi Nazia Nur, Mohammad Ahnaf Sadat","submitted_at":"2024-12-15T08:41:37Z","abstract_excerpt":"In this paper, we investigate the impact of adversarial attacks on the explainability of deep learning models, which are commonly criticized for their black-box nature despite their capacity for autonomous feature extraction. This black-box nature can affect the perceived trustworthiness of these models. To address this, explainability techniques such as GradCAM, SmoothGrad, and LIME have been developed to clarify model decision-making processes. Our research focuses on the robustness of these explanations when models are subjected to adversarial attacks, specifically those involving subtle im"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11119","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/2412.11119/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":"2412.11119","created_at":"2026-07-05T09:49:27.812105+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.11119v1","created_at":"2026-07-05T09:49:27.812105+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11119","created_at":"2026-07-05T09:49:27.812105+00:00"},{"alias_kind":"pith_short_12","alias_value":"G6Q6STL36INR","created_at":"2026-07-05T09:49:27.812105+00:00"},{"alias_kind":"pith_short_16","alias_value":"G6Q6STL36INROCET","created_at":"2026-07-05T09:49:27.812105+00:00"},{"alias_kind":"pith_short_8","alias_value":"G6Q6STL3","created_at":"2026-07-05T09:49:27.812105+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/G6Q6STL36INROCETRQWFIBGPD5","json":"https://pith.science/pith/G6Q6STL36INROCETRQWFIBGPD5.json","graph_json":"https://pith.science/api/pith-number/G6Q6STL36INROCETRQWFIBGPD5/graph.json","events_json":"https://pith.science/api/pith-number/G6Q6STL36INROCETRQWFIBGPD5/events.json","paper":"https://pith.science/paper/G6Q6STL3"},"agent_actions":{"view_html":"https://pith.science/pith/G6Q6STL36INROCETRQWFIBGPD5","download_json":"https://pith.science/pith/G6Q6STL36INROCETRQWFIBGPD5.json","view_paper":"https://pith.science/paper/G6Q6STL3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.11119&json=true","fetch_graph":"https://pith.science/api/pith-number/G6Q6STL36INROCETRQWFIBGPD5/graph.json","fetch_events":"https://pith.science/api/pith-number/G6Q6STL36INROCETRQWFIBGPD5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G6Q6STL36INROCETRQWFIBGPD5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G6Q6STL36INROCETRQWFIBGPD5/action/storage_attestation","attest_author":"https://pith.science/pith/G6Q6STL36INROCETRQWFIBGPD5/action/author_attestation","sign_citation":"https://pith.science/pith/G6Q6STL36INROCETRQWFIBGPD5/action/citation_signature","submit_replication":"https://pith.science/pith/G6Q6STL36INROCETRQWFIBGPD5/action/replication_record"}},"created_at":"2026-07-05T09:49:27.812105+00:00","updated_at":"2026-07-05T09:49:27.812105+00:00"}