{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:V4AIJQYUF4EHBJ6EI2UTTDBMJN","short_pith_number":"pith:V4AIJQYU","schema_version":"1.0","canonical_sha256":"af0084c3142f0870a7c446a9398c2c4b65cc3266853074bd7d1c6104eb8d4409","source":{"kind":"arxiv","id":"2306.06874","version":5},"attestation_state":"computed","paper":{"title":"VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Pin-Yu Chen, Sheng-Yen Chou, Tsung-Yi Ho","submitted_at":"2023-06-12T05:14:13Z","abstract_excerpt":"Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising. They are the backbone of many generative AI applications, such as text-to-image conditional generation. However, recent studies have shown that basic unconditional DMs (e.g., DDPM and DDIM) are vulnerable to backdoor injection, a type of output manipulation attack triggered by a maliciously embedded pattern at model input. This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis fo"},"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":"2306.06874","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2023-06-12T05:14:13Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"e35478642066f79be13853edd2ea301da856574d290edfa89b54f4a3b30406af","abstract_canon_sha256":"55e44698f595b20db62cb309689f7f23b865f00c476e2ce52c6402c28d733a5c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:28:44.667257Z","signature_b64":"wvnyhkRaLZePGEO/jaFE3B6fPMa3Rqv7OPklqnrszPwNdWiFTeVasrBubCeo2yhhp82N7kka1OMH54qxPR0gCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af0084c3142f0870a7c446a9398c2c4b65cc3266853074bd7d1c6104eb8d4409","last_reissued_at":"2026-07-05T07:28:44.666681Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:28:44.666681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Pin-Yu Chen, Sheng-Yen Chou, Tsung-Yi Ho","submitted_at":"2023-06-12T05:14:13Z","abstract_excerpt":"Diffusion Models (DMs) are state-of-the-art generative models that learn a reversible corruption process from iterative noise addition and denoising. They are the backbone of many generative AI applications, such as text-to-image conditional generation. However, recent studies have shown that basic unconditional DMs (e.g., DDPM and DDIM) are vulnerable to backdoor injection, a type of output manipulation attack triggered by a maliciously embedded pattern at model input. This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.06874","kind":"arxiv","version":5},"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/2306.06874/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":"2306.06874","created_at":"2026-07-05T07:28:44.666754+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.06874v5","created_at":"2026-07-05T07:28:44.666754+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.06874","created_at":"2026-07-05T07:28:44.666754+00:00"},{"alias_kind":"pith_short_12","alias_value":"V4AIJQYUF4EH","created_at":"2026-07-05T07:28:44.666754+00:00"},{"alias_kind":"pith_short_16","alias_value":"V4AIJQYUF4EHBJ6E","created_at":"2026-07-05T07:28:44.666754+00:00"},{"alias_kind":"pith_short_8","alias_value":"V4AIJQYU","created_at":"2026-07-05T07:28:44.666754+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.16512","citing_title":"TrojFlow: Flow Models are Natural Targets for Trojan Attacks","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN","json":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN.json","graph_json":"https://pith.science/api/pith-number/V4AIJQYUF4EHBJ6EI2UTTDBMJN/graph.json","events_json":"https://pith.science/api/pith-number/V4AIJQYUF4EHBJ6EI2UTTDBMJN/events.json","paper":"https://pith.science/paper/V4AIJQYU"},"agent_actions":{"view_html":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN","download_json":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN.json","view_paper":"https://pith.science/paper/V4AIJQYU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.06874&json=true","fetch_graph":"https://pith.science/api/pith-number/V4AIJQYUF4EHBJ6EI2UTTDBMJN/graph.json","fetch_events":"https://pith.science/api/pith-number/V4AIJQYUF4EHBJ6EI2UTTDBMJN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN/action/storage_attestation","attest_author":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN/action/author_attestation","sign_citation":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN/action/citation_signature","submit_replication":"https://pith.science/pith/V4AIJQYUF4EHBJ6EI2UTTDBMJN/action/replication_record"}},"created_at":"2026-07-05T07:28:44.666754+00:00","updated_at":"2026-07-05T07:28:44.666754+00:00"}