{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QPCCPKMIQITZXSDJY73VFRAX2G","short_pith_number":"pith:QPCCPKMI","canonical_record":{"source":{"id":"1906.03333","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-07T21:07:02Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"68d3433f446e70e45e3f0b67777758b633e7873ab474ff31dc8b8bf627f9d8db","abstract_canon_sha256":"414694e752913fe0a2ee003a3eba43825a3f5430e3caff325bdb769b91008114"},"schema_version":"1.0"},"canonical_sha256":"83c427a98882279bc869c7f752c417d194fff12568c498311acd8991ef0db2c0","source":{"kind":"arxiv","id":"1906.03333","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.03333","created_at":"2026-05-17T23:43:48Z"},{"alias_kind":"arxiv_version","alias_value":"1906.03333v1","created_at":"2026-05-17T23:43:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03333","created_at":"2026-05-17T23:43:48Z"},{"alias_kind":"pith_short_12","alias_value":"QPCCPKMIQITZ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QPCCPKMIQITZXSDJ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QPCCPKMI","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QPCCPKMIQITZXSDJY73VFRAX2G","target":"record","payload":{"canonical_record":{"source":{"id":"1906.03333","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-07T21:07:02Z","cross_cats_sorted":["cs.CR","stat.ML"],"title_canon_sha256":"68d3433f446e70e45e3f0b67777758b633e7873ab474ff31dc8b8bf627f9d8db","abstract_canon_sha256":"414694e752913fe0a2ee003a3eba43825a3f5430e3caff325bdb769b91008114"},"schema_version":"1.0"},"canonical_sha256":"83c427a98882279bc869c7f752c417d194fff12568c498311acd8991ef0db2c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:48.785504Z","signature_b64":"Qm0PyUruEA8HpCW4cwHESB6tYvGi15mc0Kja91OjaN4eVCqIIEpL6mRoasjMt+l1SXkpFD5DmqDawKJUh97nAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83c427a98882279bc869c7f752c417d194fff12568c498311acd8991ef0db2c0","last_reissued_at":"2026-05-17T23:43:48.785008Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:48.785008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.03333","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":"2026-05-17T23:43:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QhbBre3/iw+WS+GWP0GUDpJVf+BHl4M8uGbbVmf87zntKIbzyDvvhsi1jvGohOfd4X+WRYpqyb03sIDbzPfXAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T01:40:59.416608Z"},"content_sha256":"496f64a95a86db593c2888c7a3704fd72327b115dc9fb4cfe87d82f1181b037c","schema_version":"1.0","event_id":"sha256:496f64a95a86db593c2888c7a3704fd72327b115dc9fb4cfe87d82f1181b037c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QPCCPKMIQITZXSDJY73VFRAX2G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Project Gradient Descent for Ensemble Adversarial Attack","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bedrich Benes, Eva Haviarova, Fanyou Wu, Rado Gazo","submitted_at":"2019-06-07T21:07:02Z","abstract_excerpt":"Recent advances show that deep neural networks are not robust to deliberately crafted adversarial examples which many are generated by adding human imperceptible perturbation to clear input. Consider $l_2$ norms attacks, Project Gradient Descent (PGD) and the Carlini and Wagner (C\\&W) attacks are the two main methods, where PGD control max perturbation for adversarial examples while C\\&W approach treats perturbation as a regularization term optimized it with loss function together. If we carefully set parameters for any individual input, both methods become similar. In general, PGD attacks per"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03333","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":"2026-05-17T23:43:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6ohjqyimZfDE9FR/afIQZLAKxDpoSx0ItP8XCjV0tnRBGL4WEjMLD0q8jtEsD4E8PjU9Nc8rfByqAJ7s629dDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T01:40:59.416952Z"},"content_sha256":"64619a238672b45601bc14c1acf2770f0434ffe6636f08b2beaa333b787add52","schema_version":"1.0","event_id":"sha256:64619a238672b45601bc14c1acf2770f0434ffe6636f08b2beaa333b787add52"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QPCCPKMIQITZXSDJY73VFRAX2G/bundle.json","state_url":"https://pith.science/pith/QPCCPKMIQITZXSDJY73VFRAX2G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QPCCPKMIQITZXSDJY73VFRAX2G/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":"2026-06-20T01:40:59Z","links":{"resolver":"https://pith.science/pith/QPCCPKMIQITZXSDJY73VFRAX2G","bundle":"https://pith.science/pith/QPCCPKMIQITZXSDJY73VFRAX2G/bundle.json","state":"https://pith.science/pith/QPCCPKMIQITZXSDJY73VFRAX2G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QPCCPKMIQITZXSDJY73VFRAX2G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QPCCPKMIQITZXSDJY73VFRAX2G","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":"414694e752913fe0a2ee003a3eba43825a3f5430e3caff325bdb769b91008114","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-07T21:07:02Z","title_canon_sha256":"68d3433f446e70e45e3f0b67777758b633e7873ab474ff31dc8b8bf627f9d8db"},"schema_version":"1.0","source":{"id":"1906.03333","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.03333","created_at":"2026-05-17T23:43:48Z"},{"alias_kind":"arxiv_version","alias_value":"1906.03333v1","created_at":"2026-05-17T23:43:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.03333","created_at":"2026-05-17T23:43:48Z"},{"alias_kind":"pith_short_12","alias_value":"QPCCPKMIQITZ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"QPCCPKMIQITZXSDJ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"QPCCPKMI","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:64619a238672b45601bc14c1acf2770f0434ffe6636f08b2beaa333b787add52","target":"graph","created_at":"2026-05-17T23:43:48Z","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":"Recent advances show that deep neural networks are not robust to deliberately crafted adversarial examples which many are generated by adding human imperceptible perturbation to clear input. Consider $l_2$ norms attacks, Project Gradient Descent (PGD) and the Carlini and Wagner (C\\&W) attacks are the two main methods, where PGD control max perturbation for adversarial examples while C\\&W approach treats perturbation as a regularization term optimized it with loss function together. If we carefully set parameters for any individual input, both methods become similar. In general, PGD attacks per","authors_text":"Bedrich Benes, Eva Haviarova, Fanyou Wu, Rado Gazo","cross_cats":["cs.CR","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-07T21:07:02Z","title":"Efficient Project Gradient Descent for Ensemble Adversarial Attack"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.03333","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":"sha256:496f64a95a86db593c2888c7a3704fd72327b115dc9fb4cfe87d82f1181b037c","target":"record","created_at":"2026-05-17T23:43:48Z","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":"414694e752913fe0a2ee003a3eba43825a3f5430e3caff325bdb769b91008114","cross_cats_sorted":["cs.CR","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-07T21:07:02Z","title_canon_sha256":"68d3433f446e70e45e3f0b67777758b633e7873ab474ff31dc8b8bf627f9d8db"},"schema_version":"1.0","source":{"id":"1906.03333","kind":"arxiv","version":1}},"canonical_sha256":"83c427a98882279bc869c7f752c417d194fff12568c498311acd8991ef0db2c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"83c427a98882279bc869c7f752c417d194fff12568c498311acd8991ef0db2c0","first_computed_at":"2026-05-17T23:43:48.785008Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:48.785008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Qm0PyUruEA8HpCW4cwHESB6tYvGi15mc0Kja91OjaN4eVCqIIEpL6mRoasjMt+l1SXkpFD5DmqDawKJUh97nAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:48.785504Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.03333","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:496f64a95a86db593c2888c7a3704fd72327b115dc9fb4cfe87d82f1181b037c","sha256:64619a238672b45601bc14c1acf2770f0434ffe6636f08b2beaa333b787add52"],"state_sha256":"888181fcd04e1b1a5c384476ccf58162e3530fb7a6bdc977692fabbca4304d70"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fker5hw2iE2eyE9FmoRTS0P14TuTJeZljpzFhFgS4YdTL275RvdHxUTH0uIHuIZpyP8DdulljpFvb1MdZuVmDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T01:40:59.418989Z","bundle_sha256":"e9eb47195b8d3603000b0e33b1739f4567e7d0d8adf8617b60e11b2523fda349"}}