{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:EG3XZT77NJZYFDIRSGEXH4EI47","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":"950c44e093037fb65968cef95739757ac7efaa24a7bfb4404d3bdf50899449b9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T21:51:23Z","title_canon_sha256":"9fc1d2ddd67146a598fbc8ec9886aa616cb78dd09f68a78d71603513c9f461b2"},"schema_version":"1.0","source":{"id":"2305.16494","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.16494","created_at":"2026-07-05T07:34:33Z"},{"alias_kind":"arxiv_version","alias_value":"2305.16494v3","created_at":"2026-07-05T07:34:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16494","created_at":"2026-07-05T07:34:33Z"},{"alias_kind":"pith_short_12","alias_value":"EG3XZT77NJZY","created_at":"2026-07-05T07:34:33Z"},{"alias_kind":"pith_short_16","alias_value":"EG3XZT77NJZYFDIR","created_at":"2026-07-05T07:34:33Z"},{"alias_kind":"pith_short_8","alias_value":"EG3XZT77","created_at":"2026-07-05T07:34:33Z"}],"graph_snapshots":[{"event_id":"sha256:70b27b8d5f732e3184e7e8c66ef3ffeba40fea01c6acabd9a0d9edd579ace71a","target":"graph","created_at":"2026-07-05T07:34:33Z","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/2305.16494/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural networks are known to be susceptible to adversarial samples: small variations of natural examples crafted to deliberately mislead the models. While they can be easily generated using gradient-based techniques in digital and physical scenarios, they often differ greatly from the actual data distribution of natural images, resulting in a trade-off between strength and stealthiness. In this paper, we propose a novel framework dubbed Diffusion-Based Projected Gradient Descent (Diff-PGD) for generating realistic adversarial samples. By exploiting a gradient guided by a diffusion model, Diff-","authors_text":"Alexandre Araujo, Bin Hu, Haotian Xue, Yongxin Chen","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T21:51:23Z","title":"Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16494","kind":"arxiv","version":3},"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":"sha256:40dce939fb9e91d52df22fbd8ac5ba6046b6f4acdb0819464c9d7cfcbaa1dff2","target":"record","created_at":"2026-07-05T07:34:33Z","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":"950c44e093037fb65968cef95739757ac7efaa24a7bfb4404d3bdf50899449b9","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-05-25T21:51:23Z","title_canon_sha256":"9fc1d2ddd67146a598fbc8ec9886aa616cb78dd09f68a78d71603513c9f461b2"},"schema_version":"1.0","source":{"id":"2305.16494","kind":"arxiv","version":3}},"canonical_sha256":"21b77ccfff6a73828d11918973f088e7dbe68f76cc2be76ad2d0bc600707b73a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"21b77ccfff6a73828d11918973f088e7dbe68f76cc2be76ad2d0bc600707b73a","first_computed_at":"2026-07-05T07:34:33.395931Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:34:33.395931Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2WoRSJCPBAfXhVvgB3nLR6M/HuJm2cAhlwfb73LdN3OBZLfmakV4Z/7E1FgZBiAsA2BdCCMy5rOE3DHcXKy8Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:34:33.396527Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.16494","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:40dce939fb9e91d52df22fbd8ac5ba6046b6f4acdb0819464c9d7cfcbaa1dff2","sha256:70b27b8d5f732e3184e7e8c66ef3ffeba40fea01c6acabd9a0d9edd579ace71a"],"state_sha256":"751b9987f317e4bb6034c2699ae5451976c01c12db2b11a1acfc82aaf9cb17e9"}