{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HCMUHIXGREQ7A4QMZNKRI4YSQK","short_pith_number":"pith:HCMUHIXG","schema_version":"1.0","canonical_sha256":"389943a2e68921f0720ccb55147312828342cd49f621c65bad81a59aeceea33a","source":{"kind":"arxiv","id":"2303.04980","version":2},"attestation_state":"computed","paper":{"title":"Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Geunhyeok Yu, Hyoseok Hwang, Minwoo Jeon","submitted_at":"2023-03-09T01:42:43Z","abstract_excerpt":"The susceptibility of deep neural networks (DNNs) to adversarial examples has prompted an increase in the deployment of adversarial attacks. Image-agnostic universal adversarial perturbations (UAPs) are much more threatening, but many limitations exist to implementing UAPs in real-world scenarios where only binary decisions are returned. In this research, we propose Decision-BADGE, a novel method to craft universal adversarial perturbations for executing decision-based black-box attacks. To optimize perturbation with decisions, we addressed two challenges, namely the magnitude and the directio"},"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":"2303.04980","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-09T01:42:43Z","cross_cats_sorted":[],"title_canon_sha256":"3f95dbad8e2695557ac75d47050375a1df7076ff6577665e1b2feff364928726","abstract_canon_sha256":"1d120dc261506be78f7c97f758599efed146ecd145468a6505404f9dd669c0ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:40:51.782706Z","signature_b64":"l9RmJaExBrYIwxOWG+aryk8YzmYc4uCanHhLmqjhSDdjZSpKmkD71+ByQ1cgn3uIvexw3Kup+VH5JFJbvJWFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"389943a2e68921f0720ccb55147312828342cd49f621c65bad81a59aeceea33a","last_reissued_at":"2026-07-05T06:40:51.782283Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:40:51.782283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Geunhyeok Yu, Hyoseok Hwang, Minwoo Jeon","submitted_at":"2023-03-09T01:42:43Z","abstract_excerpt":"The susceptibility of deep neural networks (DNNs) to adversarial examples has prompted an increase in the deployment of adversarial attacks. Image-agnostic universal adversarial perturbations (UAPs) are much more threatening, but many limitations exist to implementing UAPs in real-world scenarios where only binary decisions are returned. In this research, we propose Decision-BADGE, a novel method to craft universal adversarial perturbations for executing decision-based black-box attacks. To optimize perturbation with decisions, we addressed two challenges, namely the magnitude and the directio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04980","kind":"arxiv","version":2},"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/2303.04980/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":"2303.04980","created_at":"2026-07-05T06:40:51.782341+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.04980v2","created_at":"2026-07-05T06:40:51.782341+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04980","created_at":"2026-07-05T06:40:51.782341+00:00"},{"alias_kind":"pith_short_12","alias_value":"HCMUHIXGREQ7","created_at":"2026-07-05T06:40:51.782341+00:00"},{"alias_kind":"pith_short_16","alias_value":"HCMUHIXGREQ7A4QM","created_at":"2026-07-05T06:40:51.782341+00:00"},{"alias_kind":"pith_short_8","alias_value":"HCMUHIXG","created_at":"2026-07-05T06:40:51.782341+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/HCMUHIXGREQ7A4QMZNKRI4YSQK","json":"https://pith.science/pith/HCMUHIXGREQ7A4QMZNKRI4YSQK.json","graph_json":"https://pith.science/api/pith-number/HCMUHIXGREQ7A4QMZNKRI4YSQK/graph.json","events_json":"https://pith.science/api/pith-number/HCMUHIXGREQ7A4QMZNKRI4YSQK/events.json","paper":"https://pith.science/paper/HCMUHIXG"},"agent_actions":{"view_html":"https://pith.science/pith/HCMUHIXGREQ7A4QMZNKRI4YSQK","download_json":"https://pith.science/pith/HCMUHIXGREQ7A4QMZNKRI4YSQK.json","view_paper":"https://pith.science/paper/HCMUHIXG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.04980&json=true","fetch_graph":"https://pith.science/api/pith-number/HCMUHIXGREQ7A4QMZNKRI4YSQK/graph.json","fetch_events":"https://pith.science/api/pith-number/HCMUHIXGREQ7A4QMZNKRI4YSQK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HCMUHIXGREQ7A4QMZNKRI4YSQK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HCMUHIXGREQ7A4QMZNKRI4YSQK/action/storage_attestation","attest_author":"https://pith.science/pith/HCMUHIXGREQ7A4QMZNKRI4YSQK/action/author_attestation","sign_citation":"https://pith.science/pith/HCMUHIXGREQ7A4QMZNKRI4YSQK/action/citation_signature","submit_replication":"https://pith.science/pith/HCMUHIXGREQ7A4QMZNKRI4YSQK/action/replication_record"}},"created_at":"2026-07-05T06:40:51.782341+00:00","updated_at":"2026-07-05T06:40:51.782341+00:00"}