{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:6WTYZ7SISDZKD4TH7LWAWFT3J7","short_pith_number":"pith:6WTYZ7SI","canonical_record":{"source":{"id":"2410.05951","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-08T12:05:01Z","cross_cats_sorted":[],"title_canon_sha256":"003e45aba3a083cb52c237b77c10b73daeb5ff28da18ab6778131eb964584a66","abstract_canon_sha256":"5961f6cca910c29e39e6585a86c8e89539c7c1a7828e0dcfa9b502da53464f1f"},"schema_version":"1.0"},"canonical_sha256":"f5a78cfe4890f2a1f267faec0b167b4fefab7a049633c44c222a3f058c9f6b08","source":{"kind":"arxiv","id":"2410.05951","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.05951","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"arxiv_version","alias_value":"2410.05951v1","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05951","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"pith_short_12","alias_value":"6WTYZ7SISDZK","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"pith_short_16","alias_value":"6WTYZ7SISDZKD4TH","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"pith_short_8","alias_value":"6WTYZ7SI","created_at":"2026-07-05T09:17:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:6WTYZ7SISDZKD4TH7LWAWFT3J7","target":"record","payload":{"canonical_record":{"source":{"id":"2410.05951","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-08T12:05:01Z","cross_cats_sorted":[],"title_canon_sha256":"003e45aba3a083cb52c237b77c10b73daeb5ff28da18ab6778131eb964584a66","abstract_canon_sha256":"5961f6cca910c29e39e6585a86c8e89539c7c1a7828e0dcfa9b502da53464f1f"},"schema_version":"1.0"},"canonical_sha256":"f5a78cfe4890f2a1f267faec0b167b4fefab7a049633c44c222a3f058c9f6b08","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:17:53.618779Z","signature_b64":"x8UhvYEAytJuWIjaOho/s7SwUe/ZIe2QczuWK4o0qeX0YalA+turYAtpOsrG0+BtcWc5L9WITmIpv7WLfOWqDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5a78cfe4890f2a1f267faec0b167b4fefab7a049633c44c222a3f058c9f6b08","last_reissued_at":"2026-07-05T09:17:53.618377Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:17:53.618377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.05951","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":"2026-07-05T09:17:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x+x9bBoSOUYcEkVAlg3UHr89A4XiI33aY/ZEbFjCsU+qL0wavOk00GOtogQGjfJq/5kUj2iy0tajDJD/omwSDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T14:46:44.796987Z"},"content_sha256":"ecd0ec0bb75dcbc6309931aea0f8ae786bddc3c041e030f975d088f93bd55c82","schema_version":"1.0","event_id":"sha256:ecd0ec0bb75dcbc6309931aea0f8ae786bddc3c041e030f975d088f93bd55c82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:6WTYZ7SISDZKD4TH7LWAWFT3J7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Huangsen Cao, Kainan Tu, Kangtao Lv, Xin Ding, Yihuai Xu, Yongwei Wang, Zhimeng Zhang","submitted_at":"2024-10-08T12:05:01Z","abstract_excerpt":"Large vision models have been found vulnerable to adversarial examples, emphasizing the need for enhancing their adversarial robustness. While adversarial training is an effective defense for deep convolutional models, it often faces scalability issues with large vision models due to high computational costs. Recent approaches propose robust fine-tuning methods, such as adversarial tuning of low-rank adaptation (LoRA) in large vision models, but they still struggle to match the accuracy of full parameter adversarial fine-tuning. The integration of various defense mechanisms offers a promising "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05951","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.05951/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":"2026-07-05T09:17:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2JoMIY2AkADuZuqpXD6LtB81i9Y7D/voWlQ9VHmGylLQY9tjl0M10IvA+iWvy/hhY1XQwNIdJ7q/1ymRHaLNDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T14:46:44.797568Z"},"content_sha256":"647046f0eafb2ce14520cb4df0c76b9b6d85888806efb1a390651673852771b5","schema_version":"1.0","event_id":"sha256:647046f0eafb2ce14520cb4df0c76b9b6d85888806efb1a390651673852771b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6WTYZ7SISDZKD4TH7LWAWFT3J7/bundle.json","state_url":"https://pith.science/pith/6WTYZ7SISDZKD4TH7LWAWFT3J7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6WTYZ7SISDZKD4TH7LWAWFT3J7/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":"2026-08-04T14:46:44Z","links":{"resolver":"https://pith.science/pith/6WTYZ7SISDZKD4TH7LWAWFT3J7","bundle":"https://pith.science/pith/6WTYZ7SISDZKD4TH7LWAWFT3J7/bundle.json","state":"https://pith.science/pith/6WTYZ7SISDZKD4TH7LWAWFT3J7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6WTYZ7SISDZKD4TH7LWAWFT3J7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6WTYZ7SISDZKD4TH7LWAWFT3J7","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":"5961f6cca910c29e39e6585a86c8e89539c7c1a7828e0dcfa9b502da53464f1f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-08T12:05:01Z","title_canon_sha256":"003e45aba3a083cb52c237b77c10b73daeb5ff28da18ab6778131eb964584a66"},"schema_version":"1.0","source":{"id":"2410.05951","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.05951","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"arxiv_version","alias_value":"2410.05951v1","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05951","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"pith_short_12","alias_value":"6WTYZ7SISDZK","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"pith_short_16","alias_value":"6WTYZ7SISDZKD4TH","created_at":"2026-07-05T09:17:53Z"},{"alias_kind":"pith_short_8","alias_value":"6WTYZ7SI","created_at":"2026-07-05T09:17:53Z"}],"graph_snapshots":[{"event_id":"sha256:647046f0eafb2ce14520cb4df0c76b9b6d85888806efb1a390651673852771b5","target":"graph","created_at":"2026-07-05T09:17:53Z","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/2410.05951/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large vision models have been found vulnerable to adversarial examples, emphasizing the need for enhancing their adversarial robustness. While adversarial training is an effective defense for deep convolutional models, it often faces scalability issues with large vision models due to high computational costs. Recent approaches propose robust fine-tuning methods, such as adversarial tuning of low-rank adaptation (LoRA) in large vision models, but they still struggle to match the accuracy of full parameter adversarial fine-tuning. The integration of various defense mechanisms offers a promising ","authors_text":"Huangsen Cao, Kainan Tu, Kangtao Lv, Xin Ding, Yihuai Xu, Yongwei Wang, Zhimeng Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-08T12:05:01Z","title":"Hyper Adversarial Tuning for Boosting Adversarial Robustness of Pretrained Large Vision Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05951","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:ecd0ec0bb75dcbc6309931aea0f8ae786bddc3c041e030f975d088f93bd55c82","target":"record","created_at":"2026-07-05T09:17:53Z","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":"5961f6cca910c29e39e6585a86c8e89539c7c1a7828e0dcfa9b502da53464f1f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-10-08T12:05:01Z","title_canon_sha256":"003e45aba3a083cb52c237b77c10b73daeb5ff28da18ab6778131eb964584a66"},"schema_version":"1.0","source":{"id":"2410.05951","kind":"arxiv","version":1}},"canonical_sha256":"f5a78cfe4890f2a1f267faec0b167b4fefab7a049633c44c222a3f058c9f6b08","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f5a78cfe4890f2a1f267faec0b167b4fefab7a049633c44c222a3f058c9f6b08","first_computed_at":"2026-07-05T09:17:53.618377Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:17:53.618377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"x8UhvYEAytJuWIjaOho/s7SwUe/ZIe2QczuWK4o0qeX0YalA+turYAtpOsrG0+BtcWc5L9WITmIpv7WLfOWqDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:17:53.618779Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.05951","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ecd0ec0bb75dcbc6309931aea0f8ae786bddc3c041e030f975d088f93bd55c82","sha256:647046f0eafb2ce14520cb4df0c76b9b6d85888806efb1a390651673852771b5"],"state_sha256":"cfe8199f2a52da7d232433b9a17aba02da28c9d7137558d4b3db5e0e857244f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VfPZGIFYeXzNE1wsTnUZwkzkNF4Aak9/NP96CDkkPLUiNTYNXdqwQ6bpfkmQyJveROW6BWosAT2Ne1JuRdAKDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T14:46:44.801204Z","bundle_sha256":"dbd32a920fa82c05a43e53d38ab8f02e61fe545c45ab8cdd716ffd6b49459cc0"}}