{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:SMOV4GOT7YAINYN5L5I5TWCNH7","short_pith_number":"pith:SMOV4GOT","canonical_record":{"source":{"id":"2111.09094","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-17T13:20:29Z","cross_cats_sorted":[],"title_canon_sha256":"4874e3221f0b89b35cb53d460ad839d27eda4f3d0fa772a402f4187abbc33e53","abstract_canon_sha256":"3d21a8c2ba52da7345597c3ddcd79f9db451b49e35b6034c53675772be1f24cc"},"schema_version":"1.0"},"canonical_sha256":"931d5e19d3fe0086e1bd5f51d9d84d3fc2ca2d116a7550046f764963d55704af","source":{"kind":"arxiv","id":"2111.09094","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.09094","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"arxiv_version","alias_value":"2111.09094v3","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.09094","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"pith_short_12","alias_value":"SMOV4GOT7YAI","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"pith_short_16","alias_value":"SMOV4GOT7YAINYN5","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"pith_short_8","alias_value":"SMOV4GOT","created_at":"2026-07-05T04:41:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:SMOV4GOT7YAINYN5L5I5TWCNH7","target":"record","payload":{"canonical_record":{"source":{"id":"2111.09094","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-17T13:20:29Z","cross_cats_sorted":[],"title_canon_sha256":"4874e3221f0b89b35cb53d460ad839d27eda4f3d0fa772a402f4187abbc33e53","abstract_canon_sha256":"3d21a8c2ba52da7345597c3ddcd79f9db451b49e35b6034c53675772be1f24cc"},"schema_version":"1.0"},"canonical_sha256":"931d5e19d3fe0086e1bd5f51d9d84d3fc2ca2d116a7550046f764963d55704af","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:41:10.622951Z","signature_b64":"CVcyigZy2E0ussqHzwp7lwX7eoYrbeWvASWmXLVx/n7y+VXj84l2M8ElDmQ0GHux84+/BQt1EFnD7fJkF3wnBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"931d5e19d3fe0086e1bd5f51d9d84d3fc2ca2d116a7550046f764963d55704af","last_reissued_at":"2026-07-05T04:41:10.622470Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:41:10.622470Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.09094","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:41:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CD+YJI1EKDMn51mNhURTeDoYVuwleohKWYwDpzcykvRldVar1I9amIakJCrpgNfONpsrMIIsC81j/aF2dkLxDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:10:21.225410Z"},"content_sha256":"275c7719eca945da07732535613e21f36ca5cf5a98ddb2942a981a2743799ff6","schema_version":"1.0","event_id":"sha256:275c7719eca945da07732535613e21f36ca5cf5a98ddb2942a981a2743799ff6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:SMOV4GOT7YAINYN5L5I5TWCNH7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"STEEX: Steering Counterfactual Explanations with Semantics","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"\\'Eloi Zablocki, H\\'edi Ben-Younes, Matthieu Cord, Micka\\\"el Chen, Patrick P\\'erez, Paul Jacob","submitted_at":"2021-11-17T13:20:29Z","abstract_excerpt":"As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution face portraits, synthesizing visual counterfactual explanations has recently been proposed as a way to uncover the decision mechanisms of a trained classification model. In this work, we address the problem of producing counterfactual explanations for high-quality images and complex scenes. Leveraging recent semantic-to-image models, we propose a new generative counterfactual explanation framework that produces plausi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.09094","kind":"arxiv","version":3},"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/2111.09094/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:41:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"URNpZq0uv4TCrLHeeoVlh3Ljp0Uqu2etqJorF41OOauhBTyd6+z/4kDuh1VBj+WnBfU60wyYkN54ocG1WK3jBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:10:21.225803Z"},"content_sha256":"71f8e018862a2ab1e890aa4d47df6fd1ba52446cd7d2ad4a8a3b2a1a59616e14","schema_version":"1.0","event_id":"sha256:71f8e018862a2ab1e890aa4d47df6fd1ba52446cd7d2ad4a8a3b2a1a59616e14"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SMOV4GOT7YAINYN5L5I5TWCNH7/bundle.json","state_url":"https://pith.science/pith/SMOV4GOT7YAINYN5L5I5TWCNH7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SMOV4GOT7YAINYN5L5I5TWCNH7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T19:10:21Z","links":{"resolver":"https://pith.science/pith/SMOV4GOT7YAINYN5L5I5TWCNH7","bundle":"https://pith.science/pith/SMOV4GOT7YAINYN5L5I5TWCNH7/bundle.json","state":"https://pith.science/pith/SMOV4GOT7YAINYN5L5I5TWCNH7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SMOV4GOT7YAINYN5L5I5TWCNH7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:SMOV4GOT7YAINYN5L5I5TWCNH7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3d21a8c2ba52da7345597c3ddcd79f9db451b49e35b6034c53675772be1f24cc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-17T13:20:29Z","title_canon_sha256":"4874e3221f0b89b35cb53d460ad839d27eda4f3d0fa772a402f4187abbc33e53"},"schema_version":"1.0","source":{"id":"2111.09094","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.09094","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"arxiv_version","alias_value":"2111.09094v3","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.09094","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"pith_short_12","alias_value":"SMOV4GOT7YAI","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"pith_short_16","alias_value":"SMOV4GOT7YAINYN5","created_at":"2026-07-05T04:41:10Z"},{"alias_kind":"pith_short_8","alias_value":"SMOV4GOT","created_at":"2026-07-05T04:41:10Z"}],"graph_snapshots":[{"event_id":"sha256:71f8e018862a2ab1e890aa4d47df6fd1ba52446cd7d2ad4a8a3b2a1a59616e14","target":"graph","created_at":"2026-07-05T04:41:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2111.09094/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution face portraits, synthesizing visual counterfactual explanations has recently been proposed as a way to uncover the decision mechanisms of a trained classification model. In this work, we address the problem of producing counterfactual explanations for high-quality images and complex scenes. Leveraging recent semantic-to-image models, we propose a new generative counterfactual explanation framework that produces plausi","authors_text":"\\'Eloi Zablocki, H\\'edi Ben-Younes, Matthieu Cord, Micka\\\"el Chen, Patrick P\\'erez, Paul Jacob","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-17T13:20:29Z","title":"STEEX: Steering Counterfactual Explanations with Semantics"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.09094","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:275c7719eca945da07732535613e21f36ca5cf5a98ddb2942a981a2743799ff6","target":"record","created_at":"2026-07-05T04:41:10Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3d21a8c2ba52da7345597c3ddcd79f9db451b49e35b6034c53675772be1f24cc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-11-17T13:20:29Z","title_canon_sha256":"4874e3221f0b89b35cb53d460ad839d27eda4f3d0fa772a402f4187abbc33e53"},"schema_version":"1.0","source":{"id":"2111.09094","kind":"arxiv","version":3}},"canonical_sha256":"931d5e19d3fe0086e1bd5f51d9d84d3fc2ca2d116a7550046f764963d55704af","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"931d5e19d3fe0086e1bd5f51d9d84d3fc2ca2d116a7550046f764963d55704af","first_computed_at":"2026-07-05T04:41:10.622470Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:41:10.622470Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CVcyigZy2E0ussqHzwp7lwX7eoYrbeWvASWmXLVx/n7y+VXj84l2M8ElDmQ0GHux84+/BQt1EFnD7fJkF3wnBw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:41:10.622951Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.09094","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:275c7719eca945da07732535613e21f36ca5cf5a98ddb2942a981a2743799ff6","sha256:71f8e018862a2ab1e890aa4d47df6fd1ba52446cd7d2ad4a8a3b2a1a59616e14"],"state_sha256":"135170f88ed1ab528e6f10d506bfe54b7366f3ae6d308ab58194f64affd1dda0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9TN6Q0DAHqcyx+YA4D08INqJ6cvm7ooIVcjrdhX1WQsCs+4CFzFZb/ZnQlLSJEQUHl4Wlae8/KZhRh4f3cBGDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:10:21.227762Z","bundle_sha256":"547cb7f739014479a5a1f0b46d817e0126a084244dc097e4eb40f771b4ae1ceb"}}