{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:DKVBJU5JI2O4HNXGUM72YK4KS5","short_pith_number":"pith:DKVBJU5J","canonical_record":{"source":{"id":"2310.14265","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T11:00:04Z","cross_cats_sorted":[],"title_canon_sha256":"24a4023f6511bce48e35980e5b35d27ce3f1492ab7afd0f32877629933877666","abstract_canon_sha256":"90c868d4caacf700b68ee23093b6ce22ce64b244e02fbaeb6886635c334442f4"},"schema_version":"1.0"},"canonical_sha256":"1aaa14d3a9469dc3b6e6a33fac2b8a97647fccef902a671b1c310c1369968788","source":{"kind":"arxiv","id":"2310.14265","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14265","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14265v2","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14265","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_12","alias_value":"DKVBJU5JI2O4","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_16","alias_value":"DKVBJU5JI2O4HNXG","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_8","alias_value":"DKVBJU5J","created_at":"2026-07-05T07:09:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:DKVBJU5JI2O4HNXGUM72YK4KS5","target":"record","payload":{"canonical_record":{"source":{"id":"2310.14265","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T11:00:04Z","cross_cats_sorted":[],"title_canon_sha256":"24a4023f6511bce48e35980e5b35d27ce3f1492ab7afd0f32877629933877666","abstract_canon_sha256":"90c868d4caacf700b68ee23093b6ce22ce64b244e02fbaeb6886635c334442f4"},"schema_version":"1.0"},"canonical_sha256":"1aaa14d3a9469dc3b6e6a33fac2b8a97647fccef902a671b1c310c1369968788","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:09:02.829604Z","signature_b64":"VEGIoYBgYrXRW9f97EhIInwJNuSCGNNd9wKvkYVuhMxu0F4yGKMSrEJF+oIsqP6R9fy8e0c7b3FE/9SYSBx/Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1aaa14d3a9469dc3b6e6a33fac2b8a97647fccef902a671b1c310c1369968788","last_reissued_at":"2026-07-05T07:09:02.828959Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:09:02.828959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.14265","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:09:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nBuvz6pA8ncqmDP12ZaO11pHXMUc3FyehLE0DVPA/6tqfwlRk43+8eBiOJelvcL211kWmIxd1cHIKG0GBVT0Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:20:04.929311Z"},"content_sha256":"70aead7189f3161e4c00286f13de15e4c033be7239906bd5800a65d314535752","schema_version":"1.0","event_id":"sha256:70aead7189f3161e4c00286f13de15e4c033be7239906bd5800a65d314535752"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:DKVBJU5JI2O4HNXGUM72YK4KS5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengwei Dai, Kun Li, Minxuan Lv, Songlin Hu, Wei Zhou","submitted_at":"2023-10-22T11:00:04Z","abstract_excerpt":"Neural network models are vulnerable to adversarial examples, and adversarial transferability further increases the risk of adversarial attacks. Current methods based on transferability often rely on substitute models, which can be impractical and costly in real-world scenarios due to the unavailability of training data and the victim model's structural details. In this paper, we propose a novel approach that directly constructs adversarial examples by extracting transferable features across various tasks. Our key insight is that adversarial transferability can extend across different tasks. S"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14265","kind":"arxiv","version":2},"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/2310.14265/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":"2026-07-05T07:09:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9+hn8OYua3DU1y80RsyIjwuqC+ZI3LwK/y5qUx3zjt8+YB7aJ+pM7IueqHtPxUT4kDhplSwBbkedTUfCnqQ+Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T02:20:04.930217Z"},"content_sha256":"81cc90bd6354025df8f798615c2979681c21c69b2559d3f1116218f8b8698072","schema_version":"1.0","event_id":"sha256:81cc90bd6354025df8f798615c2979681c21c69b2559d3f1116218f8b8698072"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DKVBJU5JI2O4HNXGUM72YK4KS5/bundle.json","state_url":"https://pith.science/pith/DKVBJU5JI2O4HNXGUM72YK4KS5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DKVBJU5JI2O4HNXGUM72YK4KS5/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":"2026-08-10T02:20:04Z","links":{"resolver":"https://pith.science/pith/DKVBJU5JI2O4HNXGUM72YK4KS5","bundle":"https://pith.science/pith/DKVBJU5JI2O4HNXGUM72YK4KS5/bundle.json","state":"https://pith.science/pith/DKVBJU5JI2O4HNXGUM72YK4KS5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DKVBJU5JI2O4HNXGUM72YK4KS5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:DKVBJU5JI2O4HNXGUM72YK4KS5","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":"90c868d4caacf700b68ee23093b6ce22ce64b244e02fbaeb6886635c334442f4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T11:00:04Z","title_canon_sha256":"24a4023f6511bce48e35980e5b35d27ce3f1492ab7afd0f32877629933877666"},"schema_version":"1.0","source":{"id":"2310.14265","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14265","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14265v2","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14265","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_12","alias_value":"DKVBJU5JI2O4","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_16","alias_value":"DKVBJU5JI2O4HNXG","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_8","alias_value":"DKVBJU5J","created_at":"2026-07-05T07:09:02Z"}],"graph_snapshots":[{"event_id":"sha256:81cc90bd6354025df8f798615c2979681c21c69b2559d3f1116218f8b8698072","target":"graph","created_at":"2026-07-05T07:09:02Z","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/2310.14265/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural network models are vulnerable to adversarial examples, and adversarial transferability further increases the risk of adversarial attacks. Current methods based on transferability often rely on substitute models, which can be impractical and costly in real-world scenarios due to the unavailability of training data and the victim model's structural details. In this paper, we propose a novel approach that directly constructs adversarial examples by extracting transferable features across various tasks. Our key insight is that adversarial transferability can extend across different tasks. S","authors_text":"Chengwei Dai, Kun Li, Minxuan Lv, Songlin Hu, Wei Zhou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T11:00:04Z","title":"CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14265","kind":"arxiv","version":2},"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":"sha256:70aead7189f3161e4c00286f13de15e4c033be7239906bd5800a65d314535752","target":"record","created_at":"2026-07-05T07:09:02Z","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":"90c868d4caacf700b68ee23093b6ce22ce64b244e02fbaeb6886635c334442f4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T11:00:04Z","title_canon_sha256":"24a4023f6511bce48e35980e5b35d27ce3f1492ab7afd0f32877629933877666"},"schema_version":"1.0","source":{"id":"2310.14265","kind":"arxiv","version":2}},"canonical_sha256":"1aaa14d3a9469dc3b6e6a33fac2b8a97647fccef902a671b1c310c1369968788","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1aaa14d3a9469dc3b6e6a33fac2b8a97647fccef902a671b1c310c1369968788","first_computed_at":"2026-07-05T07:09:02.828959Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:09:02.828959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VEGIoYBgYrXRW9f97EhIInwJNuSCGNNd9wKvkYVuhMxu0F4yGKMSrEJF+oIsqP6R9fy8e0c7b3FE/9SYSBx/Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:09:02.829604Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.14265","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:70aead7189f3161e4c00286f13de15e4c033be7239906bd5800a65d314535752","sha256:81cc90bd6354025df8f798615c2979681c21c69b2559d3f1116218f8b8698072"],"state_sha256":"3a3966fa2534dc494dbc0099546e2ebb0428d2cc33fdc24b29adbe89a3aa99de"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cf0w9qTMkq2QtZjE4ybqdp8l7rD8j+2kxcLFvA9LqHZrUZikBso1W4FS6BfuZtPP7BrKR+DNZeTcbxGO+wVRBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T02:20:04.939411Z","bundle_sha256":"2ef3c323c2a9141ea083d2adb711d3bbd3341b1b151cc973865923599126b99b"}}