{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GARXNJNKU226DPGKH2K6VDXNLE","short_pith_number":"pith:GARXNJNK","schema_version":"1.0","canonical_sha256":"302376a5aaa6b5e1bcca3e95ea8eed5901ed47f6b7e952658c49516e1431aaf4","source":{"kind":"arxiv","id":"1901.10258","version":2},"attestation_state":"computed","paper":{"title":"RED-Attack: Resource Efficient Decision based Attack for Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Faiq Khalid, Hassan Ali, Muhammad Abdullah Hanif, Muhammad Shafique, Rehan Ahmed, Semeen Rehman","submitted_at":"2019-01-29T12:59:37Z","abstract_excerpt":"Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them require the output probability vector. These attacks can be mitigated by concealing the output probability vector. To address this limitation, decision-based attacks have been proposed which can estimate the model but they require several thousand queries to generate a single untargeted attack image. However, in real-time attacks, resources and attack time are very crucial parameters. Therefore, in resource-constrain"},"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":"1901.10258","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-01-29T12:59:37Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"41da64eba99b97001930a26ee6b49e09556335cfe8d6f7547bd59408c6d18298","abstract_canon_sha256":"9cd8bba2b88797f0e23a7381ceb25d71ff24328f515befa4943351bc88a378c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:02.877041Z","signature_b64":"5jw+uy0JggpfOrCFlNxCNiKF1cZkWU9RYpk6f5CPr9BVKrhyoMybmwGDGSLnzKviBnJTCGyBCsCo6OXhxYtwAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"302376a5aaa6b5e1bcca3e95ea8eed5901ed47f6b7e952658c49516e1431aaf4","last_reissued_at":"2026-05-17T23:55:02.876417Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:02.876417Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RED-Attack: Resource Efficient Decision based Attack for Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Faiq Khalid, Hassan Ali, Muhammad Abdullah Hanif, Muhammad Shafique, Rehan Ahmed, Semeen Rehman","submitted_at":"2019-01-29T12:59:37Z","abstract_excerpt":"Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them require the output probability vector. These attacks can be mitigated by concealing the output probability vector. To address this limitation, decision-based attacks have been proposed which can estimate the model but they require several thousand queries to generate a single untargeted attack image. However, in real-time attacks, resources and attack time are very crucial parameters. Therefore, in resource-constrain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.10258","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":""},"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":"1901.10258","created_at":"2026-05-17T23:55:02.876515+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.10258v2","created_at":"2026-05-17T23:55:02.876515+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.10258","created_at":"2026-05-17T23:55:02.876515+00:00"},{"alias_kind":"pith_short_12","alias_value":"GARXNJNKU226","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"GARXNJNKU226DPGK","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"GARXNJNK","created_at":"2026-05-18T12:33:18.533446+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.05587","citing_title":"Stateful Detection of Black-Box Adversarial Attacks","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE","json":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE.json","graph_json":"https://pith.science/api/pith-number/GARXNJNKU226DPGKH2K6VDXNLE/graph.json","events_json":"https://pith.science/api/pith-number/GARXNJNKU226DPGKH2K6VDXNLE/events.json","paper":"https://pith.science/paper/GARXNJNK"},"agent_actions":{"view_html":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE","download_json":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE.json","view_paper":"https://pith.science/paper/GARXNJNK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.10258&json=true","fetch_graph":"https://pith.science/api/pith-number/GARXNJNKU226DPGKH2K6VDXNLE/graph.json","fetch_events":"https://pith.science/api/pith-number/GARXNJNKU226DPGKH2K6VDXNLE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE/action/storage_attestation","attest_author":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE/action/author_attestation","sign_citation":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE/action/citation_signature","submit_replication":"https://pith.science/pith/GARXNJNKU226DPGKH2K6VDXNLE/action/replication_record"}},"created_at":"2026-05-17T23:55:02.876515+00:00","updated_at":"2026-05-17T23:55:02.876515+00:00"}