{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XX4TBR7KLFR3J4YNTIEK5VF66S","short_pith_number":"pith:XX4TBR7K","schema_version":"1.0","canonical_sha256":"bdf930c7ea5963b4f30d9a08aed4bef49b9e58b62c663ea8199a14cc6c7a3bf1","source":{"kind":"arxiv","id":"2405.06278","version":1},"attestation_state":"computed","paper":{"title":"Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CV","authors_text":"Amira Guesmi, Muhammad Shafique, Nishant Suresh Aswani","submitted_at":"2024-05-10T07:21:03Z","abstract_excerpt":"Adversarial attacks pose a significant challenge to deploying deep learning models in safety-critical applications. Maintaining model robustness while ensuring interpretability is vital for fostering trust and comprehension in these models. This study investigates the impact of Saliency-guided Training (SGT) on model robustness, a technique aimed at improving the clarity of saliency maps to deepen understanding of the model's decision-making process. Experiments were conducted on standard benchmark datasets using various deep learning architectures trained with and without SGT. Findings demons"},"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":"2405.06278","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-05-10T07:21:03Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"7ae316b70b30e2cadfbfb3644a791d74d0da3c69241795009be173093638f146","abstract_canon_sha256":"04cdadeb8bf20b2b5c4b846083f2a9acc58c975b79ffee677cbd01c7a6e26973"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:17:38.391729Z","signature_b64":"Qo0KqpGb9mStPCdphoF2u7llu8VnME7vLEdDwo0MEvciyZzN11meaf93EXpM7Du1fO6e/I7aTwLySUIlICNgAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdf930c7ea5963b4f30d9a08aed4bef49b9e58b62c663ea8199a14cc6c7a3bf1","last_reissued_at":"2026-07-05T08:17:38.391261Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:17:38.391261Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Interplay of Interpretability and Robustness in Deep Neural Networks: A Saliency-guided Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CV","authors_text":"Amira Guesmi, Muhammad Shafique, Nishant Suresh Aswani","submitted_at":"2024-05-10T07:21:03Z","abstract_excerpt":"Adversarial attacks pose a significant challenge to deploying deep learning models in safety-critical applications. Maintaining model robustness while ensuring interpretability is vital for fostering trust and comprehension in these models. This study investigates the impact of Saliency-guided Training (SGT) on model robustness, a technique aimed at improving the clarity of saliency maps to deepen understanding of the model's decision-making process. Experiments were conducted on standard benchmark datasets using various deep learning architectures trained with and without SGT. Findings demons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.06278","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/2405.06278/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":"2405.06278","created_at":"2026-07-05T08:17:38.391321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.06278v1","created_at":"2026-07-05T08:17:38.391321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.06278","created_at":"2026-07-05T08:17:38.391321+00:00"},{"alias_kind":"pith_short_12","alias_value":"XX4TBR7KLFR3","created_at":"2026-07-05T08:17:38.391321+00:00"},{"alias_kind":"pith_short_16","alias_value":"XX4TBR7KLFR3J4YN","created_at":"2026-07-05T08:17:38.391321+00:00"},{"alias_kind":"pith_short_8","alias_value":"XX4TBR7K","created_at":"2026-07-05T08:17:38.391321+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/XX4TBR7KLFR3J4YNTIEK5VF66S","json":"https://pith.science/pith/XX4TBR7KLFR3J4YNTIEK5VF66S.json","graph_json":"https://pith.science/api/pith-number/XX4TBR7KLFR3J4YNTIEK5VF66S/graph.json","events_json":"https://pith.science/api/pith-number/XX4TBR7KLFR3J4YNTIEK5VF66S/events.json","paper":"https://pith.science/paper/XX4TBR7K"},"agent_actions":{"view_html":"https://pith.science/pith/XX4TBR7KLFR3J4YNTIEK5VF66S","download_json":"https://pith.science/pith/XX4TBR7KLFR3J4YNTIEK5VF66S.json","view_paper":"https://pith.science/paper/XX4TBR7K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.06278&json=true","fetch_graph":"https://pith.science/api/pith-number/XX4TBR7KLFR3J4YNTIEK5VF66S/graph.json","fetch_events":"https://pith.science/api/pith-number/XX4TBR7KLFR3J4YNTIEK5VF66S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XX4TBR7KLFR3J4YNTIEK5VF66S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XX4TBR7KLFR3J4YNTIEK5VF66S/action/storage_attestation","attest_author":"https://pith.science/pith/XX4TBR7KLFR3J4YNTIEK5VF66S/action/author_attestation","sign_citation":"https://pith.science/pith/XX4TBR7KLFR3J4YNTIEK5VF66S/action/citation_signature","submit_replication":"https://pith.science/pith/XX4TBR7KLFR3J4YNTIEK5VF66S/action/replication_record"}},"created_at":"2026-07-05T08:17:38.391321+00:00","updated_at":"2026-07-05T08:17:38.391321+00:00"}