{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:DDZIEIN3N6SN2KRZSJRNVTKSAI","short_pith_number":"pith:DDZIEIN3","schema_version":"1.0","canonical_sha256":"18f28221bb6fa4dd2a399262dacd52022242dcf60becd05b6ebb84f0094a121e","source":{"kind":"arxiv","id":"1609.00804","version":1},"attestation_state":"computed","paper":{"title":"Randomized Prediction Games for Adversarial Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.LG","authors_text":"Battista Biggio, Fabio Roli, Ignazio Pillai, Marcello Pelillo, Samuel Rota Bul\\`o","submitted_at":"2016-09-03T09:30:51Z","abstract_excerpt":"In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time; e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the class"},"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":"1609.00804","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-09-03T09:30:51Z","cross_cats_sorted":["cs.GT"],"title_canon_sha256":"5f398c00f6188a6c1e01066e1571480a11ac2f13eceacdffe8b85c634436aa13","abstract_canon_sha256":"2b63181a090a0cd8b7dbf4d114f9e39d0e1a5581fef97890d6f7cad4fe19c86f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:05:41.454282Z","signature_b64":"okjWEufaJ8Wc6riwAypleNW9l0+3XdR/syNN1XjhoADSYaZnNbDydlXtlZKJ5J9sEAbP5m9yI1Bg2/gNu+0XDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"18f28221bb6fa4dd2a399262dacd52022242dcf60becd05b6ebb84f0094a121e","last_reissued_at":"2026-05-18T01:05:41.453771Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:05:41.453771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Randomized Prediction Games for Adversarial Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT"],"primary_cat":"cs.LG","authors_text":"Battista Biggio, Fabio Roli, Ignazio Pillai, Marcello Pelillo, Samuel Rota Bul\\`o","submitted_at":"2016-09-03T09:30:51Z","abstract_excerpt":"In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time; e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the class"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.00804","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":"1609.00804","created_at":"2026-05-18T01:05:41.453856+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.00804v1","created_at":"2026-05-18T01:05:41.453856+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.00804","created_at":"2026-05-18T01:05:41.453856+00:00"},{"alias_kind":"pith_short_12","alias_value":"DDZIEIN3N6SN","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"DDZIEIN3N6SN2KRZ","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"DDZIEIN3","created_at":"2026-05-18T12:30:12.583610+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/DDZIEIN3N6SN2KRZSJRNVTKSAI","json":"https://pith.science/pith/DDZIEIN3N6SN2KRZSJRNVTKSAI.json","graph_json":"https://pith.science/api/pith-number/DDZIEIN3N6SN2KRZSJRNVTKSAI/graph.json","events_json":"https://pith.science/api/pith-number/DDZIEIN3N6SN2KRZSJRNVTKSAI/events.json","paper":"https://pith.science/paper/DDZIEIN3"},"agent_actions":{"view_html":"https://pith.science/pith/DDZIEIN3N6SN2KRZSJRNVTKSAI","download_json":"https://pith.science/pith/DDZIEIN3N6SN2KRZSJRNVTKSAI.json","view_paper":"https://pith.science/paper/DDZIEIN3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.00804&json=true","fetch_graph":"https://pith.science/api/pith-number/DDZIEIN3N6SN2KRZSJRNVTKSAI/graph.json","fetch_events":"https://pith.science/api/pith-number/DDZIEIN3N6SN2KRZSJRNVTKSAI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DDZIEIN3N6SN2KRZSJRNVTKSAI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DDZIEIN3N6SN2KRZSJRNVTKSAI/action/storage_attestation","attest_author":"https://pith.science/pith/DDZIEIN3N6SN2KRZSJRNVTKSAI/action/author_attestation","sign_citation":"https://pith.science/pith/DDZIEIN3N6SN2KRZSJRNVTKSAI/action/citation_signature","submit_replication":"https://pith.science/pith/DDZIEIN3N6SN2KRZSJRNVTKSAI/action/replication_record"}},"created_at":"2026-05-18T01:05:41.453856+00:00","updated_at":"2026-05-18T01:05:41.453856+00:00"}