{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:6TN35VIBL3L364KYAYLNEXDJNY","short_pith_number":"pith:6TN35VIB","canonical_record":{"source":{"id":"2505.08999","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-13T22:26:19Z","cross_cats_sorted":[],"title_canon_sha256":"d281eca3df2adb9885451ced6417bb38811e3feaf1b89a7868f8fcc5c6f37f6c","abstract_canon_sha256":"db2b31d5b4e53ad1b077156d3dfcf56bb7b1bf23944e3fc0c7810c782a830790"},"schema_version":"1.0"},"canonical_sha256":"f4dbbed5015ed7bf71580616d25c696e07150a2d49a8660320fcfadd66d3ba62","source":{"kind":"arxiv","id":"2505.08999","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.08999","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.08999v1","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08999","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_12","alias_value":"6TN35VIBL3L3","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_16","alias_value":"6TN35VIBL3L364KY","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_8","alias_value":"6TN35VIB","created_at":"2026-07-05T11:02:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:6TN35VIBL3L364KYAYLNEXDJNY","target":"record","payload":{"canonical_record":{"source":{"id":"2505.08999","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-13T22:26:19Z","cross_cats_sorted":[],"title_canon_sha256":"d281eca3df2adb9885451ced6417bb38811e3feaf1b89a7868f8fcc5c6f37f6c","abstract_canon_sha256":"db2b31d5b4e53ad1b077156d3dfcf56bb7b1bf23944e3fc0c7810c782a830790"},"schema_version":"1.0"},"canonical_sha256":"f4dbbed5015ed7bf71580616d25c696e07150a2d49a8660320fcfadd66d3ba62","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:02:48.579627Z","signature_b64":"lFRsA9aJTrgB4YGQqwpRcmNTIVhInt907y7E5uYlvTyBQECNdH8yLmq0BmObqPXy3Wq0oTdzuwuNRda1cLUOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f4dbbed5015ed7bf71580616d25c696e07150a2d49a8660320fcfadd66d3ba62","last_reissued_at":"2026-07-05T11:02:48.579221Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:02:48.579221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.08999","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":"2026-07-05T11:02:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PhDwo1mDr/d7tkZWZ8JsRP+WOkk2BMxD5lD49SxCmqWewtBBmHjqPbpOVS/Br4hwFBKIlc/zzp4Ds75zRZHfAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T13:43:16.945818Z"},"content_sha256":"e360bc1b4834015542143a070e17c7edeb0deb050e1bbfaed3284334ba95f65e","schema_version":"1.0","event_id":"sha256:e360bc1b4834015542143a070e17c7edeb0deb050e1bbfaed3284334ba95f65e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:6TN35VIBL3L364KYAYLNEXDJNY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards Adaptive Meta-Gradient Adversarial Examples for Visual Tracking","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hamido Fujita, Hanan Aljuai, Long Xu, Mao-Li Wang, Peng Gao, Wei-Long Tian, Xiao Liu","submitted_at":"2025-05-13T22:26:19Z","abstract_excerpt":"In recent years, visual tracking methods based on convolutional neural networks and Transformers have achieved remarkable performance and have been successfully applied in fields such as autonomous driving. However, the numerous security issues exposed by deep learning models have gradually affected the reliable application of visual tracking methods in real-world scenarios. Therefore, how to reveal the security vulnerabilities of existing visual trackers through effective adversarial attacks has become a critical problem that needs to be addressed. To this end, we propose an adaptive meta-gra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08999","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/2505.08999/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":"2026-07-05T11:02:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K6mPFLEbKM7ik3h5m+RPN6lov4rvbP40qKFw1EZRaDOfutxG9r2d3gVkdHBL00/FF/NRzJuYj2RZ0J0i+C/JBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T13:43:16.947094Z"},"content_sha256":"b0b84026ae53e91acfa7ee5636cbc8360f3cadf906c31333450c0f107f4dd5bc","schema_version":"1.0","event_id":"sha256:b0b84026ae53e91acfa7ee5636cbc8360f3cadf906c31333450c0f107f4dd5bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6TN35VIBL3L364KYAYLNEXDJNY/bundle.json","state_url":"https://pith.science/pith/6TN35VIBL3L364KYAYLNEXDJNY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6TN35VIBL3L364KYAYLNEXDJNY/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":"2026-08-19T13:43:16Z","links":{"resolver":"https://pith.science/pith/6TN35VIBL3L364KYAYLNEXDJNY","bundle":"https://pith.science/pith/6TN35VIBL3L364KYAYLNEXDJNY/bundle.json","state":"https://pith.science/pith/6TN35VIBL3L364KYAYLNEXDJNY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6TN35VIBL3L364KYAYLNEXDJNY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:6TN35VIBL3L364KYAYLNEXDJNY","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":"db2b31d5b4e53ad1b077156d3dfcf56bb7b1bf23944e3fc0c7810c782a830790","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-13T22:26:19Z","title_canon_sha256":"d281eca3df2adb9885451ced6417bb38811e3feaf1b89a7868f8fcc5c6f37f6c"},"schema_version":"1.0","source":{"id":"2505.08999","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.08999","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"arxiv_version","alias_value":"2505.08999v1","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.08999","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_12","alias_value":"6TN35VIBL3L3","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_16","alias_value":"6TN35VIBL3L364KY","created_at":"2026-07-05T11:02:48Z"},{"alias_kind":"pith_short_8","alias_value":"6TN35VIB","created_at":"2026-07-05T11:02:48Z"}],"graph_snapshots":[{"event_id":"sha256:b0b84026ae53e91acfa7ee5636cbc8360f3cadf906c31333450c0f107f4dd5bc","target":"graph","created_at":"2026-07-05T11:02:48Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2505.08999/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, visual tracking methods based on convolutional neural networks and Transformers have achieved remarkable performance and have been successfully applied in fields such as autonomous driving. However, the numerous security issues exposed by deep learning models have gradually affected the reliable application of visual tracking methods in real-world scenarios. Therefore, how to reveal the security vulnerabilities of existing visual trackers through effective adversarial attacks has become a critical problem that needs to be addressed. To this end, we propose an adaptive meta-gra","authors_text":"Hamido Fujita, Hanan Aljuai, Long Xu, Mao-Li Wang, Peng Gao, Wei-Long Tian, Xiao Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-13T22:26:19Z","title":"Towards Adaptive Meta-Gradient Adversarial Examples for Visual Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.08999","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:e360bc1b4834015542143a070e17c7edeb0deb050e1bbfaed3284334ba95f65e","target":"record","created_at":"2026-07-05T11:02:48Z","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":"db2b31d5b4e53ad1b077156d3dfcf56bb7b1bf23944e3fc0c7810c782a830790","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-13T22:26:19Z","title_canon_sha256":"d281eca3df2adb9885451ced6417bb38811e3feaf1b89a7868f8fcc5c6f37f6c"},"schema_version":"1.0","source":{"id":"2505.08999","kind":"arxiv","version":1}},"canonical_sha256":"f4dbbed5015ed7bf71580616d25c696e07150a2d49a8660320fcfadd66d3ba62","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f4dbbed5015ed7bf71580616d25c696e07150a2d49a8660320fcfadd66d3ba62","first_computed_at":"2026-07-05T11:02:48.579221Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:02:48.579221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lFRsA9aJTrgB4YGQqwpRcmNTIVhInt907y7E5uYlvTyBQECNdH8yLmq0BmObqPXy3Wq0oTdzuwuNRda1cLUOBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:02:48.579627Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.08999","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e360bc1b4834015542143a070e17c7edeb0deb050e1bbfaed3284334ba95f65e","sha256:b0b84026ae53e91acfa7ee5636cbc8360f3cadf906c31333450c0f107f4dd5bc"],"state_sha256":"197799b13634590eaa80a8afb4e2b6c6d956609cdb7786bc77b48d58f978fc85"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6piYeWHIFbV/NAkagzSbu/Jqr6Px8N1x2xLt/AzVMcFkmMy394EdCmpTUEM/Ya1oOT8fbzki1+w/Vf3NkeLACA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T13:43:16.953011Z","bundle_sha256":"e5a8b45c0da67cb65c6d2798913bedd7ef4fedb2653fc6a5dd1ede72ee1e3188"}}