{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:354YQASWGO2SIG7UL7JMUVURHA","short_pith_number":"pith:354YQASW","schema_version":"1.0","canonical_sha256":"df7988025633b5241bf45fd2ca569138015dce46999d2552665113cb7b14833a","source":{"kind":"arxiv","id":"1907.07291","version":1},"attestation_state":"computed","paper":{"title":"Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arif Siddiqi","submitted_at":"2019-07-17T00:00:07Z","abstract_excerpt":"The ever-growing big data and emerging artificial intelligence (AI) demand the use of machine learning (ML) and deep learning (DL) methods. Cybersecurity also benefits from ML and DL methods for various types of applications. These methods however are susceptible to security attacks. The adversaries can exploit the training and testing data of the learning models or can explore the workings of those models for launching advanced future attacks. The topic of adversarial security attacks and perturbations within the ML and DL domains is a recent exploration and a great interest is expressed by 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":"1907.07291","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-07-17T00:00:07Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"760267bdc4a1a90b0add753d23d6af11b7229f82eb5916afa88690148e7dc277","abstract_canon_sha256":"f55a3b672c904e38c86e9ed344a4f5ae34045d4d0f7bc2cb1d5b8f03a70ce1fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:22.784061Z","signature_b64":"OKHSQyF7LyvNSt5HGR44mJ1RTyEtEGXyb9lv4wJkqRy9/OaDoweM/MkKMSpjNVU0cYdB6/LAfVjOxLtggKtlCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df7988025633b5241bf45fd2ca569138015dce46999d2552665113cb7b14833a","last_reissued_at":"2026-05-17T23:40:22.783299Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:22.783299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arif Siddiqi","submitted_at":"2019-07-17T00:00:07Z","abstract_excerpt":"The ever-growing big data and emerging artificial intelligence (AI) demand the use of machine learning (ML) and deep learning (DL) methods. Cybersecurity also benefits from ML and DL methods for various types of applications. These methods however are susceptible to security attacks. The adversaries can exploit the training and testing data of the learning models or can explore the workings of those models for launching advanced future attacks. The topic of adversarial security attacks and perturbations within the ML and DL domains is a recent exploration and a great interest is expressed by t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.07291","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":""},"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":"1907.07291","created_at":"2026-05-17T23:40:22.783428+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.07291v1","created_at":"2026-05-17T23:40:22.783428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.07291","created_at":"2026-05-17T23:40:22.783428+00:00"},{"alias_kind":"pith_short_12","alias_value":"354YQASWGO2S","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"354YQASWGO2SIG7U","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"354YQASW","created_at":"2026-05-18T12:33:07.085635+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/354YQASWGO2SIG7UL7JMUVURHA","json":"https://pith.science/pith/354YQASWGO2SIG7UL7JMUVURHA.json","graph_json":"https://pith.science/api/pith-number/354YQASWGO2SIG7UL7JMUVURHA/graph.json","events_json":"https://pith.science/api/pith-number/354YQASWGO2SIG7UL7JMUVURHA/events.json","paper":"https://pith.science/paper/354YQASW"},"agent_actions":{"view_html":"https://pith.science/pith/354YQASWGO2SIG7UL7JMUVURHA","download_json":"https://pith.science/pith/354YQASWGO2SIG7UL7JMUVURHA.json","view_paper":"https://pith.science/paper/354YQASW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.07291&json=true","fetch_graph":"https://pith.science/api/pith-number/354YQASWGO2SIG7UL7JMUVURHA/graph.json","fetch_events":"https://pith.science/api/pith-number/354YQASWGO2SIG7UL7JMUVURHA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/354YQASWGO2SIG7UL7JMUVURHA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/354YQASWGO2SIG7UL7JMUVURHA/action/storage_attestation","attest_author":"https://pith.science/pith/354YQASWGO2SIG7UL7JMUVURHA/action/author_attestation","sign_citation":"https://pith.science/pith/354YQASWGO2SIG7UL7JMUVURHA/action/citation_signature","submit_replication":"https://pith.science/pith/354YQASWGO2SIG7UL7JMUVURHA/action/replication_record"}},"created_at":"2026-05-17T23:40:22.783428+00:00","updated_at":"2026-05-17T23:40:22.783428+00:00"}