{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZOFTEVPWP4WJEE2VOHVHQHBBLG","short_pith_number":"pith:ZOFTEVPW","schema_version":"1.0","canonical_sha256":"cb8b3255f67f2c92135571ea781c2159af9de2ceb9628ef3e34c1712d6ad31c2","source":{"kind":"arxiv","id":"2307.12499","version":4},"attestation_state":"computed","paper":{"title":"AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Bin Xiao, Kaisheng Liang, Xuelong Dai","submitted_at":"2023-07-24T03:10:02Z","abstract_excerpt":"Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applications because they can effectively bypass defense mechanisms. However, previous attack methods often directly inject Projected Gradient Descent (PGD) gradients into the sampling of generative models, which are not theoretically provable and thus generate unrealistic examples by incorporating adversarial objectives, especially for GAN-based methods on large-scale datasets like ImageNet. In this paper, we propose a new m"},"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":"2307.12499","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-07-24T03:10:02Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"d2dc2de2a9811bd502a4b6f8f9957df74c1449935935632b0eaa9cd078f4c6b5","abstract_canon_sha256":"fd1331f060acd18ce7d0fcace5e195f46db8f97cdc4f652f77a52864228b6c0d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:43:22.878400Z","signature_b64":"u2ctuLbmyBr6c6VJBiwHgXQoSmvhPBZOydsO9WYTcUzh5d/wpAJi+ZvYn1DE/CefOu55+2hubGASKMQGAV4VBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb8b3255f67f2c92135571ea781c2159af9de2ceb9628ef3e34c1712d6ad31c2","last_reissued_at":"2026-07-05T08:43:22.877967Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:43:22.877967Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Bin Xiao, Kaisheng Liang, Xuelong Dai","submitted_at":"2023-07-24T03:10:02Z","abstract_excerpt":"Unrestricted adversarial attacks present a serious threat to deep learning models and adversarial defense techniques. They pose severe security problems for deep learning applications because they can effectively bypass defense mechanisms. However, previous attack methods often directly inject Projected Gradient Descent (PGD) gradients into the sampling of generative models, which are not theoretically provable and thus generate unrealistic examples by incorporating adversarial objectives, especially for GAN-based methods on large-scale datasets like ImageNet. In this paper, we propose a new m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.12499","kind":"arxiv","version":4},"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/2307.12499/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":"2307.12499","created_at":"2026-07-05T08:43:22.878019+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.12499v4","created_at":"2026-07-05T08:43:22.878019+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.12499","created_at":"2026-07-05T08:43:22.878019+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZOFTEVPWP4WJ","created_at":"2026-07-05T08:43:22.878019+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZOFTEVPWP4WJEE2V","created_at":"2026-07-05T08:43:22.878019+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZOFTEVPW","created_at":"2026-07-05T08:43:22.878019+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26566","citing_title":"Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG","json":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG.json","graph_json":"https://pith.science/api/pith-number/ZOFTEVPWP4WJEE2VOHVHQHBBLG/graph.json","events_json":"https://pith.science/api/pith-number/ZOFTEVPWP4WJEE2VOHVHQHBBLG/events.json","paper":"https://pith.science/paper/ZOFTEVPW"},"agent_actions":{"view_html":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG","download_json":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG.json","view_paper":"https://pith.science/paper/ZOFTEVPW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.12499&json=true","fetch_graph":"https://pith.science/api/pith-number/ZOFTEVPWP4WJEE2VOHVHQHBBLG/graph.json","fetch_events":"https://pith.science/api/pith-number/ZOFTEVPWP4WJEE2VOHVHQHBBLG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG/action/storage_attestation","attest_author":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG/action/author_attestation","sign_citation":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG/action/citation_signature","submit_replication":"https://pith.science/pith/ZOFTEVPWP4WJEE2VOHVHQHBBLG/action/replication_record"}},"created_at":"2026-07-05T08:43:22.878019+00:00","updated_at":"2026-07-05T08:43:22.878019+00:00"}