{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WF6HV5EY63GNCXKMAQABURP6YD","short_pith_number":"pith:WF6HV5EY","schema_version":"1.0","canonical_sha256":"b17c7af498f6ccd15d4c04001a45fec0d717ba1159efea6bc5c78f2015d23211","source":{"kind":"arxiv","id":"2411.09265","version":1},"attestation_state":"computed","paper":{"title":"BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangliang Cheng, Qi Zhao, Shuchang Lyu, Wenquan Feng, Xiaowei Huang, Zheng Zhou","submitted_at":"2024-11-14T08:05:34Z","abstract_excerpt":"Dataset Distillation (DD) is an emerging technique that compresses large-scale datasets into significantly smaller synthesized datasets while preserving high test performance and enabling the efficient training of large models. However, current research primarily focuses on enhancing evaluation accuracy under limited compression ratios, often overlooking critical security concerns such as adversarial robustness. A key challenge in evaluating this robustness lies in the complex interactions between distillation methods, model architectures, and adversarial attack strategies, which complicate st"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.09265","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-14T08:05:34Z","cross_cats_sorted":[],"title_canon_sha256":"0fe0d8a56c72d4bcad5aad97a3fb0437e781b33c9270230d110a2b12d4d93698","abstract_canon_sha256":"af0a5590123add2eada90759b0c2a618db6e98acce5b847d26e4dfd1ad2b81e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:35:23.485994Z","signature_b64":"0n3v0YRJ9UZ48Evgf7RMFOBdnqhPhp5rcGfNvRDjGZ8JlJFL3l+dwim4j/N2rhyKFbxDHs52zaou4vMptVzdCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b17c7af498f6ccd15d4c04001a45fec0d717ba1159efea6bc5c78f2015d23211","last_reissued_at":"2026-07-05T09:35:23.485517Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:35:23.485517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangliang Cheng, Qi Zhao, Shuchang Lyu, Wenquan Feng, Xiaowei Huang, Zheng Zhou","submitted_at":"2024-11-14T08:05:34Z","abstract_excerpt":"Dataset Distillation (DD) is an emerging technique that compresses large-scale datasets into significantly smaller synthesized datasets while preserving high test performance and enabling the efficient training of large models. However, current research primarily focuses on enhancing evaluation accuracy under limited compression ratios, often overlooking critical security concerns such as adversarial robustness. A key challenge in evaluating this robustness lies in the complex interactions between distillation methods, model architectures, and adversarial attack strategies, which complicate st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09265","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/2411.09265/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.09265","created_at":"2026-07-05T09:35:23.485580+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.09265v1","created_at":"2026-07-05T09:35:23.485580+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09265","created_at":"2026-07-05T09:35:23.485580+00:00"},{"alias_kind":"pith_short_12","alias_value":"WF6HV5EY63GN","created_at":"2026-07-05T09:35:23.485580+00:00"},{"alias_kind":"pith_short_16","alias_value":"WF6HV5EY63GNCXKM","created_at":"2026-07-05T09:35:23.485580+00:00"},{"alias_kind":"pith_short_8","alias_value":"WF6HV5EY","created_at":"2026-07-05T09:35:23.485580+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD","json":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD.json","graph_json":"https://pith.science/api/pith-number/WF6HV5EY63GNCXKMAQABURP6YD/graph.json","events_json":"https://pith.science/api/pith-number/WF6HV5EY63GNCXKMAQABURP6YD/events.json","paper":"https://pith.science/paper/WF6HV5EY"},"agent_actions":{"view_html":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD","download_json":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD.json","view_paper":"https://pith.science/paper/WF6HV5EY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.09265&json=true","fetch_graph":"https://pith.science/api/pith-number/WF6HV5EY63GNCXKMAQABURP6YD/graph.json","fetch_events":"https://pith.science/api/pith-number/WF6HV5EY63GNCXKMAQABURP6YD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD/action/storage_attestation","attest_author":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD/action/author_attestation","sign_citation":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD/action/citation_signature","submit_replication":"https://pith.science/pith/WF6HV5EY63GNCXKMAQABURP6YD/action/replication_record"}},"created_at":"2026-07-05T09:35:23.485580+00:00","updated_at":"2026-07-05T09:35:23.485580+00:00"}