{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:EM6GJOPJMZZNG4H3VO3N6METB7","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":"f8f95ce769e3b619540aab8a3e91161b9aef06530ac19497220ea05e4e7f79e6","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-08T23:00:06Z","title_canon_sha256":"23cb0b8ed43093345d53abadc1f1230c665e4ae3ebd1e03e48222b9298fdd70f"},"schema_version":"1.0","source":{"id":"1810.03739","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.03739","created_at":"2026-05-18T00:03:44Z"},{"alias_kind":"arxiv_version","alias_value":"1810.03739v1","created_at":"2026-05-18T00:03:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.03739","created_at":"2026-05-18T00:03:44Z"},{"alias_kind":"pith_short_12","alias_value":"EM6GJOPJMZZN","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EM6GJOPJMZZNG4H3","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EM6GJOPJ","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:76364bb361139bbaa71316893e1c7fc5252887f2b799f50f377ce6429515bef1","target":"graph","created_at":"2026-05-18T00:03:44Z","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"},"paper":{"abstract_excerpt":"In recent years, deep neural networks have demonstrated outstanding performance in many machine learning tasks. However, researchers have discovered that these state-of-the-art models are vulnerable to adversarial examples: legitimate examples added by small perturbations which are unnoticeable to human eyes. Adversarial training, which augments the training data with adversarial examples during the training process, is a well known defense to improve the robustness of the model against adversarial attacks. However, this robustness is only effective to the same attack method used for adversari","authors_text":"Peng Li, Ting-Jui Chang, Yukun He","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-08T23:00:06Z","title":"Efficient Two-Step Adversarial Defense for Deep Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.03739","kind":"arxiv","version":1},"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:bb9db73ea0fc03d452458e7fbbba5c99a3bd14636e2090a609cfc06046239aa3","target":"record","created_at":"2026-05-18T00:03:44Z","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":"f8f95ce769e3b619540aab8a3e91161b9aef06530ac19497220ea05e4e7f79e6","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-08T23:00:06Z","title_canon_sha256":"23cb0b8ed43093345d53abadc1f1230c665e4ae3ebd1e03e48222b9298fdd70f"},"schema_version":"1.0","source":{"id":"1810.03739","kind":"arxiv","version":1}},"canonical_sha256":"233c64b9e96672d370fbabb6df30930feed710a63bc17237fd2bbcff53c6576a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"233c64b9e96672d370fbabb6df30930feed710a63bc17237fd2bbcff53c6576a","first_computed_at":"2026-05-18T00:03:44.616137Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:03:44.616137Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wc0GbQvDBAuDaTC7qi3qM3eus7ZfIrzH//I7i0eW81zIdFaunGRpKGKOUpIaSJv4xPnmXZ2EGdENUcoZUUWiCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:03:44.616592Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.03739","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb9db73ea0fc03d452458e7fbbba5c99a3bd14636e2090a609cfc06046239aa3","sha256:76364bb361139bbaa71316893e1c7fc5252887f2b799f50f377ce6429515bef1"],"state_sha256":"dd69fba6573c993741ea1577a4379984563988e246ab736e7d3d541c3d825034"}