{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QUM3X2ADSEZAS6WYGNXJESB2JS","short_pith_number":"pith:QUM3X2AD","schema_version":"1.0","canonical_sha256":"8519bbe8039132097ad8336e92483a4c921986e19f704e5b4e1e67953616d17f","source":{"kind":"arxiv","id":"2411.14681","version":2},"attestation_state":"computed","paper":{"title":"TrojanEdit: Multimodal Backdoor Attack Against Image Editing Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Aiguo Chen, Guoming Lu, Hongwei Li, Jiachen Li, Jiaming He, Ji Guo, Peihong Chen, Wenbo Jiang, Xiaolei Wen","submitted_at":"2024-11-22T02:27:27Z","abstract_excerpt":"Multimodal diffusion models for image editing generate outputs conditioned on both textual instructions and visual inputs, aiming to modify target regions while preserving the rest of the image. Although diffusion models have been shown to be vulnerable to backdoor attacks, existing efforts mainly focus on unimodal generative models and fail to address the unique challenges in multimodal image editing. In this paper, we present the first study of backdoor attacks on multimodal diffusion-based image editing models. We investigate the use of both textual and visual triggers to embed a backdoor t"},"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":"2411.14681","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2024-11-22T02:27:27Z","cross_cats_sorted":[],"title_canon_sha256":"6f3f9e4a2c552550459479a8993c069b33bc5b7be68a1a0ecfc999cdaef1c5ea","abstract_canon_sha256":"a1a65e4d7e9a0f0c7ea2fd207b745b9515102799ca1f2d1f234aa9f90ff5273a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:22.501425Z","signature_b64":"yxM4TVBSQKyparBjPAkSkCQRWtgeUbNbtFr/GZhMYNtLEyVS5F8CmvlWTPegr1W/uVN2R0K2MHUucoqCJf4VBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8519bbe8039132097ad8336e92483a4c921986e19f704e5b4e1e67953616d17f","last_reissued_at":"2026-07-05T11:12:22.500902Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:22.500902Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TrojanEdit: Multimodal Backdoor Attack Against Image Editing Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Aiguo Chen, Guoming Lu, Hongwei Li, Jiachen Li, Jiaming He, Ji Guo, Peihong Chen, Wenbo Jiang, Xiaolei Wen","submitted_at":"2024-11-22T02:27:27Z","abstract_excerpt":"Multimodal diffusion models for image editing generate outputs conditioned on both textual instructions and visual inputs, aiming to modify target regions while preserving the rest of the image. Although diffusion models have been shown to be vulnerable to backdoor attacks, existing efforts mainly focus on unimodal generative models and fail to address the unique challenges in multimodal image editing. In this paper, we present the first study of backdoor attacks on multimodal diffusion-based image editing models. We investigate the use of both textual and visual triggers to embed a backdoor t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14681","kind":"arxiv","version":2},"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/2411.14681/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":"2411.14681","created_at":"2026-07-05T11:12:22.500958+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14681v2","created_at":"2026-07-05T11:12:22.500958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14681","created_at":"2026-07-05T11:12:22.500958+00:00"},{"alias_kind":"pith_short_12","alias_value":"QUM3X2ADSEZA","created_at":"2026-07-05T11:12:22.500958+00:00"},{"alias_kind":"pith_short_16","alias_value":"QUM3X2ADSEZAS6WY","created_at":"2026-07-05T11:12:22.500958+00:00"},{"alias_kind":"pith_short_8","alias_value":"QUM3X2AD","created_at":"2026-07-05T11:12:22.500958+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04879","citing_title":"Invisible Backdoor Triggers in Image Editing Model via Deep Watermarking","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS","json":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS.json","graph_json":"https://pith.science/api/pith-number/QUM3X2ADSEZAS6WYGNXJESB2JS/graph.json","events_json":"https://pith.science/api/pith-number/QUM3X2ADSEZAS6WYGNXJESB2JS/events.json","paper":"https://pith.science/paper/QUM3X2AD"},"agent_actions":{"view_html":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS","download_json":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS.json","view_paper":"https://pith.science/paper/QUM3X2AD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14681&json=true","fetch_graph":"https://pith.science/api/pith-number/QUM3X2ADSEZAS6WYGNXJESB2JS/graph.json","fetch_events":"https://pith.science/api/pith-number/QUM3X2ADSEZAS6WYGNXJESB2JS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS/action/storage_attestation","attest_author":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS/action/author_attestation","sign_citation":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS/action/citation_signature","submit_replication":"https://pith.science/pith/QUM3X2ADSEZAS6WYGNXJESB2JS/action/replication_record"}},"created_at":"2026-07-05T11:12:22.500958+00:00","updated_at":"2026-07-05T11:12:22.500958+00:00"}