{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:D44BYOZO5OXA7BSIN4V565GZ6G","short_pith_number":"pith:D44BYOZO","schema_version":"1.0","canonical_sha256":"1f381c3b2eebae0f86486f2bdf74d9f1b742fb7082803c5ea9e2d0792dd67f97","source":{"kind":"arxiv","id":"2501.06841","version":1},"attestation_state":"computed","paper":{"title":"Faithful Counterfactual Visual Explanations (FCVE)","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bismillah Khan, David Windridge, Muhammad Ahsan, Syed Ali Tariq, Tehseen Zia","submitted_at":"2025-01-12T15:18:31Z","abstract_excerpt":"Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations that non-experts easily understand, and they cannot accurately identify models' intrinsic decision-making processes. To address these challenges, we propose to develop a counterfactual explanation (CE) model that balances plausibility and faithfulness. This model generates easy-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":"2501.06841","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-12T15:18:31Z","cross_cats_sorted":[],"title_canon_sha256":"c2d8b9847b428d8dffe6c6c16fde0749370d92812294ae0e81b9b35f65c64a34","abstract_canon_sha256":"1731552090caa02e2736a79f7ba93ddd587cbf62e769f28188646941432fb14d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:10.991954Z","signature_b64":"7V36t2RNu8Y0qyc7T3Ff4MmrzOHtYmU3Eit+JOg05QWuqxlelQK2HsTVlyWhvr7m2FOBTZYuNEkvqilHEkjIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f381c3b2eebae0f86486f2bdf74d9f1b742fb7082803c5ea9e2d0792dd67f97","last_reissued_at":"2026-07-05T10:00:10.991606Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:10.991606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Faithful Counterfactual Visual Explanations (FCVE)","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bismillah Khan, David Windridge, Muhammad Ahsan, Syed Ali Tariq, Tehseen Zia","submitted_at":"2025-01-12T15:18:31Z","abstract_excerpt":"Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations that non-experts easily understand, and they cannot accurately identify models' intrinsic decision-making processes. To address these challenges, we propose to develop a counterfactual explanation (CE) model that balances plausibility and faithfulness. This model generates easy-t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06841","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/2501.06841/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":"2501.06841","created_at":"2026-07-05T10:00:10.991662+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.06841v1","created_at":"2026-07-05T10:00:10.991662+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06841","created_at":"2026-07-05T10:00:10.991662+00:00"},{"alias_kind":"pith_short_12","alias_value":"D44BYOZO5OXA","created_at":"2026-07-05T10:00:10.991662+00:00"},{"alias_kind":"pith_short_16","alias_value":"D44BYOZO5OXA7BSI","created_at":"2026-07-05T10:00:10.991662+00:00"},{"alias_kind":"pith_short_8","alias_value":"D44BYOZO","created_at":"2026-07-05T10:00:10.991662+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/D44BYOZO5OXA7BSIN4V565GZ6G","json":"https://pith.science/pith/D44BYOZO5OXA7BSIN4V565GZ6G.json","graph_json":"https://pith.science/api/pith-number/D44BYOZO5OXA7BSIN4V565GZ6G/graph.json","events_json":"https://pith.science/api/pith-number/D44BYOZO5OXA7BSIN4V565GZ6G/events.json","paper":"https://pith.science/paper/D44BYOZO"},"agent_actions":{"view_html":"https://pith.science/pith/D44BYOZO5OXA7BSIN4V565GZ6G","download_json":"https://pith.science/pith/D44BYOZO5OXA7BSIN4V565GZ6G.json","view_paper":"https://pith.science/paper/D44BYOZO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.06841&json=true","fetch_graph":"https://pith.science/api/pith-number/D44BYOZO5OXA7BSIN4V565GZ6G/graph.json","fetch_events":"https://pith.science/api/pith-number/D44BYOZO5OXA7BSIN4V565GZ6G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D44BYOZO5OXA7BSIN4V565GZ6G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D44BYOZO5OXA7BSIN4V565GZ6G/action/storage_attestation","attest_author":"https://pith.science/pith/D44BYOZO5OXA7BSIN4V565GZ6G/action/author_attestation","sign_citation":"https://pith.science/pith/D44BYOZO5OXA7BSIN4V565GZ6G/action/citation_signature","submit_replication":"https://pith.science/pith/D44BYOZO5OXA7BSIN4V565GZ6G/action/replication_record"}},"created_at":"2026-07-05T10:00:10.991662+00:00","updated_at":"2026-07-05T10:00:10.991662+00:00"}