{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FXRZYHXA4AN5CZ5WVO7WP7B73F","short_pith_number":"pith:FXRZYHXA","canonical_record":{"source":{"id":"2505.21742","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T20:32:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ce9b3694f74ee3a0dc0544b2afdae9a12751614e64186f7cd7ade673bffde4e0","abstract_canon_sha256":"d5d5ddd7c252e844a91ab9cc18d09e2ff8182a75a5f48e65a5756521264b4198"},"schema_version":"1.0"},"canonical_sha256":"2de39c1ee0e01bd167b6abbf67fc3fd95764b0d19089df7489f33e4354576c54","source":{"kind":"arxiv","id":"2505.21742","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.21742","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"arxiv_version","alias_value":"2505.21742v1","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21742","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"pith_short_12","alias_value":"FXRZYHXA4AN5","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"pith_short_16","alias_value":"FXRZYHXA4AN5CZ5W","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"pith_short_8","alias_value":"FXRZYHXA","created_at":"2026-07-05T11:11:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FXRZYHXA4AN5CZ5WVO7WP7B73F","target":"record","payload":{"canonical_record":{"source":{"id":"2505.21742","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T20:32:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ce9b3694f74ee3a0dc0544b2afdae9a12751614e64186f7cd7ade673bffde4e0","abstract_canon_sha256":"d5d5ddd7c252e844a91ab9cc18d09e2ff8182a75a5f48e65a5756521264b4198"},"schema_version":"1.0"},"canonical_sha256":"2de39c1ee0e01bd167b6abbf67fc3fd95764b0d19089df7489f33e4354576c54","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:11:02.792141Z","signature_b64":"fSmtXTisVXYhqtSzEHI1clNa2Y3Oel2eC7DRbPZDMfrw8YS+4gI/FjHoHuaPDYGzHIh4wpVvUmMQcj/xF3v/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2de39c1ee0e01bd167b6abbf67fc3fd95764b0d19089df7489f33e4354576c54","last_reissued_at":"2026-07-05T11:11:02.791691Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:11:02.791691Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.21742","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":"2026-07-05T11:11:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rd+xCBFXliIOryWF6AHJW7COxEK7tJyH9EpVi+66iMpyV6wM20jK3SMv7ZyBvdQOt774yg9iXlcp5lXuW0R4Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T23:14:29.516680Z"},"content_sha256":"ed366ad60024454fe09033d6d55f6d81f712d34b4582fff98814ad2c0020673d","schema_version":"1.0","event_id":"sha256:ed366ad60024454fe09033d6d55f6d81f712d34b4582fff98814ad2c0020673d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FXRZYHXA4AN5CZ5WVO7WP7B73F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"What is Adversarial Training for Diffusion Models?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Briglia Maria Rosaria, Giuseppe Lisanti, Iacopo Masi, Mujtaba Hussain Mirza","submitted_at":"2025-05-27T20:32:28Z","abstract_excerpt":"We answer the question in the title, showing that adversarial training (AT) for diffusion models (DMs) fundamentally differs from classifiers: while AT in classifiers enforces output invariance, AT in DMs requires equivariance to keep the diffusion process aligned with the data distribution. AT is a way to enforce smoothness in the diffusion flow, improving robustness to outliers and corrupted data. Unlike prior art, our method makes no assumptions about the noise model and integrates seamlessly into diffusion training by adding random noise, similar to randomized smoothing, or adversarial noi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21742","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/2505.21742/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":"2026-07-05T11:11:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R4wT5+zrrQsLl3R9OMEZb0r5R1aEsFI1lrs60dDm+E9mSMDXQZadcCW0FT+ClsoRcKG4w8ePSWJzsCRwROdBAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T23:14:29.517592Z"},"content_sha256":"ac56509d53400ac8797861988ffe496b40dcb5d6eb77d138612cb057546eadcb","schema_version":"1.0","event_id":"sha256:ac56509d53400ac8797861988ffe496b40dcb5d6eb77d138612cb057546eadcb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FXRZYHXA4AN5CZ5WVO7WP7B73F/bundle.json","state_url":"https://pith.science/pith/FXRZYHXA4AN5CZ5WVO7WP7B73F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FXRZYHXA4AN5CZ5WVO7WP7B73F/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":"2026-08-09T23:14:29Z","links":{"resolver":"https://pith.science/pith/FXRZYHXA4AN5CZ5WVO7WP7B73F","bundle":"https://pith.science/pith/FXRZYHXA4AN5CZ5WVO7WP7B73F/bundle.json","state":"https://pith.science/pith/FXRZYHXA4AN5CZ5WVO7WP7B73F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FXRZYHXA4AN5CZ5WVO7WP7B73F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FXRZYHXA4AN5CZ5WVO7WP7B73F","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":"d5d5ddd7c252e844a91ab9cc18d09e2ff8182a75a5f48e65a5756521264b4198","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T20:32:28Z","title_canon_sha256":"ce9b3694f74ee3a0dc0544b2afdae9a12751614e64186f7cd7ade673bffde4e0"},"schema_version":"1.0","source":{"id":"2505.21742","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.21742","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"arxiv_version","alias_value":"2505.21742v1","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.21742","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"pith_short_12","alias_value":"FXRZYHXA4AN5","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"pith_short_16","alias_value":"FXRZYHXA4AN5CZ5W","created_at":"2026-07-05T11:11:02Z"},{"alias_kind":"pith_short_8","alias_value":"FXRZYHXA","created_at":"2026-07-05T11:11:02Z"}],"graph_snapshots":[{"event_id":"sha256:ac56509d53400ac8797861988ffe496b40dcb5d6eb77d138612cb057546eadcb","target":"graph","created_at":"2026-07-05T11:11:02Z","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/2505.21742/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We answer the question in the title, showing that adversarial training (AT) for diffusion models (DMs) fundamentally differs from classifiers: while AT in classifiers enforces output invariance, AT in DMs requires equivariance to keep the diffusion process aligned with the data distribution. AT is a way to enforce smoothness in the diffusion flow, improving robustness to outliers and corrupted data. Unlike prior art, our method makes no assumptions about the noise model and integrates seamlessly into diffusion training by adding random noise, similar to randomized smoothing, or adversarial noi","authors_text":"Briglia Maria Rosaria, Giuseppe Lisanti, Iacopo Masi, Mujtaba Hussain Mirza","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T20:32:28Z","title":"What is Adversarial Training for Diffusion Models?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.21742","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:ed366ad60024454fe09033d6d55f6d81f712d34b4582fff98814ad2c0020673d","target":"record","created_at":"2026-07-05T11:11:02Z","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":"d5d5ddd7c252e844a91ab9cc18d09e2ff8182a75a5f48e65a5756521264b4198","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-27T20:32:28Z","title_canon_sha256":"ce9b3694f74ee3a0dc0544b2afdae9a12751614e64186f7cd7ade673bffde4e0"},"schema_version":"1.0","source":{"id":"2505.21742","kind":"arxiv","version":1}},"canonical_sha256":"2de39c1ee0e01bd167b6abbf67fc3fd95764b0d19089df7489f33e4354576c54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2de39c1ee0e01bd167b6abbf67fc3fd95764b0d19089df7489f33e4354576c54","first_computed_at":"2026-07-05T11:11:02.791691Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:11:02.791691Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fSmtXTisVXYhqtSzEHI1clNa2Y3Oel2eC7DRbPZDMfrw8YS+4gI/FjHoHuaPDYGzHIh4wpVvUmMQcj/xF3v/DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:11:02.792141Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.21742","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ed366ad60024454fe09033d6d55f6d81f712d34b4582fff98814ad2c0020673d","sha256:ac56509d53400ac8797861988ffe496b40dcb5d6eb77d138612cb057546eadcb"],"state_sha256":"3080b3da07777db6c7923367b098c0f36f5cca7b1b1e84461d071aa1797516a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NNohE8mUmn5k/ADRRy+3H4IzBuaZG3cnPrTevJL5/HI/qTh0MHvs/fDrZU+k4nom9sFM0HogKhSLtwX3b98kDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T23:14:29.525771Z","bundle_sha256":"c9191780243c178ed5bb1e4913d6765d44c5d6f0cba8765085c80e9b544fe942"}}