{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:4NCJSGREHF2JA2GAUUCUYUG3LJ","short_pith_number":"pith:4NCJSGRE","canonical_record":{"source":{"id":"1801.10578","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-31T17:51:32Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"f24e8bb7b016dcc82d501fa1d2eca438151a35ca2b116372e02bb8c5db6f2eed","abstract_canon_sha256":"c3ca939fdf3bea3e0c0d74ec8e599a2fe68dbe94bdeac13e44c3c7fa929812ce"},"schema_version":"1.0"},"canonical_sha256":"e344991a2439749068c0a5054c50db5a56af239a550168336590d396e1837ab4","source":{"kind":"arxiv","id":"1801.10578","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.10578","created_at":"2026-05-18T00:24:40Z"},{"alias_kind":"arxiv_version","alias_value":"1801.10578v1","created_at":"2026-05-18T00:24:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.10578","created_at":"2026-05-18T00:24:40Z"},{"alias_kind":"pith_short_12","alias_value":"4NCJSGREHF2J","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4NCJSGREHF2JA2GA","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4NCJSGRE","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:4NCJSGREHF2JA2GAUUCUYUG3LJ","target":"record","payload":{"canonical_record":{"source":{"id":"1801.10578","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-31T17:51:32Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"f24e8bb7b016dcc82d501fa1d2eca438151a35ca2b116372e02bb8c5db6f2eed","abstract_canon_sha256":"c3ca939fdf3bea3e0c0d74ec8e599a2fe68dbe94bdeac13e44c3c7fa929812ce"},"schema_version":"1.0"},"canonical_sha256":"e344991a2439749068c0a5054c50db5a56af239a550168336590d396e1837ab4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:40.441911Z","signature_b64":"ws3t13erSxP2X2b69MHid4pSvVP6Bcp9CtR5zmm7ugxZwKBHq1fbNm9NivdSI6LUB97604O5cJMmYqqp+ZVHAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e344991a2439749068c0a5054c50db5a56af239a550168336590d396e1837ab4","last_reissued_at":"2026-05-18T00:24:40.441294Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:40.441294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.10578","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H4oeXLt7RBYrgZmKws7vZZovbV4rqimlVpEJrB/jlDzwzmn8xcQla1c2wPFTROFSbfDyWR37P9AWhd9AMwk2BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T10:15:12.739102Z"},"content_sha256":"a040480e389cad1a81cdb96e95ef03d29def3db5bd1dd8a32cf82fd68a0dfbc1","schema_version":"1.0","event_id":"sha256:a040480e389cad1a81cdb96e95ef03d29def3db5bd1dd8a32cf82fd68a0dfbc1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:4NCJSGREHF2JA2GAUUCUYUG3LJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"stat.ML","authors_text":"Cho-Jui Hsieh, Dong Su, Huan Zhang, Jinfeng Yi, Luca Daniel, Pin-Yu Chen, Tsui-Wei Weng, Yupeng Gao","submitted_at":"2018-01-31T17:51:32Z","abstract_excerpt":"The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness. In this paper, we provide a theoretical justification for converting robustness analysis into a local Lipschitz constant estimation problem, and propose to use the Extreme Value Theory for efficient evaluation. Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Valu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.10578","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YlEjscyJkSeEiucY4kFwM++c2k/lsqZwAyPcHjGSuu+Jp/1v6IErDLJGI05tbGc8cdzMkiqYzqdd8ycH9UlXBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T10:15:12.739475Z"},"content_sha256":"be67d3ead6ff6f503d0a945443847fb5f188ce4b8492e70e4070197d3a8f3cef","schema_version":"1.0","event_id":"sha256:be67d3ead6ff6f503d0a945443847fb5f188ce4b8492e70e4070197d3a8f3cef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4NCJSGREHF2JA2GAUUCUYUG3LJ/bundle.json","state_url":"https://pith.science/pith/4NCJSGREHF2JA2GAUUCUYUG3LJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4NCJSGREHF2JA2GAUUCUYUG3LJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T10:15:12Z","links":{"resolver":"https://pith.science/pith/4NCJSGREHF2JA2GAUUCUYUG3LJ","bundle":"https://pith.science/pith/4NCJSGREHF2JA2GAUUCUYUG3LJ/bundle.json","state":"https://pith.science/pith/4NCJSGREHF2JA2GAUUCUYUG3LJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4NCJSGREHF2JA2GAUUCUYUG3LJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:4NCJSGREHF2JA2GAUUCUYUG3LJ","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":"c3ca939fdf3bea3e0c0d74ec8e599a2fe68dbe94bdeac13e44c3c7fa929812ce","cross_cats_sorted":["cs.CR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-31T17:51:32Z","title_canon_sha256":"f24e8bb7b016dcc82d501fa1d2eca438151a35ca2b116372e02bb8c5db6f2eed"},"schema_version":"1.0","source":{"id":"1801.10578","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.10578","created_at":"2026-05-18T00:24:40Z"},{"alias_kind":"arxiv_version","alias_value":"1801.10578v1","created_at":"2026-05-18T00:24:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.10578","created_at":"2026-05-18T00:24:40Z"},{"alias_kind":"pith_short_12","alias_value":"4NCJSGREHF2J","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"4NCJSGREHF2JA2GA","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"4NCJSGRE","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:be67d3ead6ff6f503d0a945443847fb5f188ce4b8492e70e4070197d3a8f3cef","target":"graph","created_at":"2026-05-18T00:24:40Z","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":"The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness. In this paper, we provide a theoretical justification for converting robustness analysis into a local Lipschitz constant estimation problem, and propose to use the Extreme Value Theory for efficient evaluation. Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Valu","authors_text":"Cho-Jui Hsieh, Dong Su, Huan Zhang, Jinfeng Yi, Luca Daniel, Pin-Yu Chen, Tsui-Wei Weng, Yupeng Gao","cross_cats":["cs.CR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-31T17:51:32Z","title":"Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.10578","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:a040480e389cad1a81cdb96e95ef03d29def3db5bd1dd8a32cf82fd68a0dfbc1","target":"record","created_at":"2026-05-18T00:24:40Z","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":"c3ca939fdf3bea3e0c0d74ec8e599a2fe68dbe94bdeac13e44c3c7fa929812ce","cross_cats_sorted":["cs.CR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-31T17:51:32Z","title_canon_sha256":"f24e8bb7b016dcc82d501fa1d2eca438151a35ca2b116372e02bb8c5db6f2eed"},"schema_version":"1.0","source":{"id":"1801.10578","kind":"arxiv","version":1}},"canonical_sha256":"e344991a2439749068c0a5054c50db5a56af239a550168336590d396e1837ab4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e344991a2439749068c0a5054c50db5a56af239a550168336590d396e1837ab4","first_computed_at":"2026-05-18T00:24:40.441294Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:40.441294Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ws3t13erSxP2X2b69MHid4pSvVP6Bcp9CtR5zmm7ugxZwKBHq1fbNm9NivdSI6LUB97604O5cJMmYqqp+ZVHAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:40.441911Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.10578","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a040480e389cad1a81cdb96e95ef03d29def3db5bd1dd8a32cf82fd68a0dfbc1","sha256:be67d3ead6ff6f503d0a945443847fb5f188ce4b8492e70e4070197d3a8f3cef"],"state_sha256":"054f9a065ab5e39e91f4e5dda9f9b129d6e5cb24f0de247fd73b5afcc0e9ab2a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0KI+eHPGu6l6gKbXvyLNQt0eJfcnY8ATuZPkAraPBZ9St244LjLUEd/hLaJYycutlAXMH9mf7DXqvQtosIFjAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T10:15:12.741604Z","bundle_sha256":"b73cba3fbaead8bfb8f9fc89c001c1aba37666f40518fdaff26272b0637c213b"}}