{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:JOCEFCRDLCE2MGUIFRHCHLBJW7","short_pith_number":"pith:JOCEFCRD","canonical_record":{"source":{"id":"2002.10097","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-24T07:28:43Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cc73d88bc68ba1adc8fff830b91ad1be1b638be329f978b89b586a1786526feb","abstract_canon_sha256":"339c2b166043ab2888fd0741d934fddde55b1b4bc5f36a5f658b77005c0fac1b"},"schema_version":"1.0"},"canonical_sha256":"4b84428a235889a61a882c4e23ac29b7f616d62aff1a1fc2967dc0132e0be478","source":{"kind":"arxiv","id":"2002.10097","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.10097","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"arxiv_version","alias_value":"2002.10097v4","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.10097","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"pith_short_12","alias_value":"JOCEFCRDLCE2","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"pith_short_16","alias_value":"JOCEFCRDLCE2MGUI","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"pith_short_8","alias_value":"JOCEFCRD","created_at":"2026-07-05T00:48:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:JOCEFCRDLCE2MGUIFRHCHLBJW7","target":"record","payload":{"canonical_record":{"source":{"id":"2002.10097","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-24T07:28:43Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"cc73d88bc68ba1adc8fff830b91ad1be1b638be329f978b89b586a1786526feb","abstract_canon_sha256":"339c2b166043ab2888fd0741d934fddde55b1b4bc5f36a5f658b77005c0fac1b"},"schema_version":"1.0"},"canonical_sha256":"4b84428a235889a61a882c4e23ac29b7f616d62aff1a1fc2967dc0132e0be478","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:48:24.548140Z","signature_b64":"lTa7ViR1LtLon0f8es9ri5MJkOcKJZipWOoHoAt+HDByrepXjdnbWD+LyYPNnLbKVeN/6OK1lgoXZw7R14YvDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b84428a235889a61a882c4e23ac29b7f616d62aff1a1fc2967dc0132e0be478","last_reissued_at":"2026-07-05T00:48:24.547627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:48:24.547627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2002.10097","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:48:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZyZ1gRQn6diqvvCvgCVmoWKJft6IEw2oX2LZvTqBLowJi+ySM054Hq//Oo93aRccA16QQy6Fi6KJ5NjD29UEDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:55:12.064090Z"},"content_sha256":"4998b65e7999212a089c7b94ab517aa1ee45ce4cf6379714a1b922c0b168568f","schema_version":"1.0","event_id":"sha256:4998b65e7999212a089c7b94ab517aa1ee45ce4cf6379714a1b922c0b168568f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:JOCEFCRDLCE2MGUIFRHCHLBJW7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Rapid and Robust Adversarial Training with One-Step Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Bj\\\"orn Eskofier, Leo Schwinn, Ren\\'e Raab","submitted_at":"2020-02-24T07:28:43Z","abstract_excerpt":"Adversarial training is the most successful empirical method for increasing the robustness of neural networks against adversarial attacks. However, the most effective approaches, like training with Projected Gradient Descent (PGD) are accompanied by high computational complexity. In this paper, we present two ideas that, in combination, enable adversarial training with the computationally less expensive Fast Gradient Sign Method (FGSM). First, we add uniform noise to the initial data point of the FGSM attack, which creates a wider variety of adversaries, thus prohibiting overfitting to one par"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.10097","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2002.10097/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:48:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7Y6vfIocYa3X35fLjj4wdt4GscuCE5wiwZbqJMlvF2QOFD/JQaCmaeq8C6dPZzDkeKCsAunhUNEikXfy9aa7Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:55:12.064471Z"},"content_sha256":"7817c263222476081b2e6574edb4aa3ee4cfee74f403f3fe5562fa16da61b262","schema_version":"1.0","event_id":"sha256:7817c263222476081b2e6574edb4aa3ee4cfee74f403f3fe5562fa16da61b262"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JOCEFCRDLCE2MGUIFRHCHLBJW7/bundle.json","state_url":"https://pith.science/pith/JOCEFCRDLCE2MGUIFRHCHLBJW7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JOCEFCRDLCE2MGUIFRHCHLBJW7/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":"2026-07-07T03:55:12Z","links":{"resolver":"https://pith.science/pith/JOCEFCRDLCE2MGUIFRHCHLBJW7","bundle":"https://pith.science/pith/JOCEFCRDLCE2MGUIFRHCHLBJW7/bundle.json","state":"https://pith.science/pith/JOCEFCRDLCE2MGUIFRHCHLBJW7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JOCEFCRDLCE2MGUIFRHCHLBJW7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:JOCEFCRDLCE2MGUIFRHCHLBJW7","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":"339c2b166043ab2888fd0741d934fddde55b1b4bc5f36a5f658b77005c0fac1b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-24T07:28:43Z","title_canon_sha256":"cc73d88bc68ba1adc8fff830b91ad1be1b638be329f978b89b586a1786526feb"},"schema_version":"1.0","source":{"id":"2002.10097","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.10097","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"arxiv_version","alias_value":"2002.10097v4","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.10097","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"pith_short_12","alias_value":"JOCEFCRDLCE2","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"pith_short_16","alias_value":"JOCEFCRDLCE2MGUI","created_at":"2026-07-05T00:48:24Z"},{"alias_kind":"pith_short_8","alias_value":"JOCEFCRD","created_at":"2026-07-05T00:48:24Z"}],"graph_snapshots":[{"event_id":"sha256:7817c263222476081b2e6574edb4aa3ee4cfee74f403f3fe5562fa16da61b262","target":"graph","created_at":"2026-07-05T00:48:24Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2002.10097/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Adversarial training is the most successful empirical method for increasing the robustness of neural networks against adversarial attacks. However, the most effective approaches, like training with Projected Gradient Descent (PGD) are accompanied by high computational complexity. In this paper, we present two ideas that, in combination, enable adversarial training with the computationally less expensive Fast Gradient Sign Method (FGSM). First, we add uniform noise to the initial data point of the FGSM attack, which creates a wider variety of adversaries, thus prohibiting overfitting to one par","authors_text":"Bj\\\"orn Eskofier, Leo Schwinn, Ren\\'e Raab","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-24T07:28:43Z","title":"Towards Rapid and Robust Adversarial Training with One-Step Attacks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.10097","kind":"arxiv","version":4},"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":"sha256:4998b65e7999212a089c7b94ab517aa1ee45ce4cf6379714a1b922c0b168568f","target":"record","created_at":"2026-07-05T00:48:24Z","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":"339c2b166043ab2888fd0741d934fddde55b1b4bc5f36a5f658b77005c0fac1b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-02-24T07:28:43Z","title_canon_sha256":"cc73d88bc68ba1adc8fff830b91ad1be1b638be329f978b89b586a1786526feb"},"schema_version":"1.0","source":{"id":"2002.10097","kind":"arxiv","version":4}},"canonical_sha256":"4b84428a235889a61a882c4e23ac29b7f616d62aff1a1fc2967dc0132e0be478","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4b84428a235889a61a882c4e23ac29b7f616d62aff1a1fc2967dc0132e0be478","first_computed_at":"2026-07-05T00:48:24.547627Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:48:24.547627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lTa7ViR1LtLon0f8es9ri5MJkOcKJZipWOoHoAt+HDByrepXjdnbWD+LyYPNnLbKVeN/6OK1lgoXZw7R14YvDA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:48:24.548140Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.10097","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4998b65e7999212a089c7b94ab517aa1ee45ce4cf6379714a1b922c0b168568f","sha256:7817c263222476081b2e6574edb4aa3ee4cfee74f403f3fe5562fa16da61b262"],"state_sha256":"0079965f91eda0a02e04ef695d49181c4dd70ee9f3d4e436b2f419d22579f369"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7ixMlL1beSQaz3ujU2/NcXJqla2QuhgJgG4M0rFoVSE6lxoKOHAHMn6zcoOmQMZh6kQIFiBLAGajzkJ1cCJfCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T03:55:12.066409Z","bundle_sha256":"4048d82da156b1cde33a93b3ca18b7221aad7a7aa7e48c022e387d3b3728128f"}}