{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PCTQ4F66ANAJWSUKXR6IT2B4BF","short_pith_number":"pith:PCTQ4F66","schema_version":"1.0","canonical_sha256":"78a70e17de03409b4a8abc7c89e83c096699fdb0ab125f65670017f1f754e6a7","source":{"kind":"arxiv","id":"2306.13103","version":1},"attestation_state":"computed","paper":{"title":"Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Hao Zhang, Hongcheng Gao, Yinpeng Dong, Zhijie Deng","submitted_at":"2023-06-16T00:43:35Z","abstract_excerpt":"Text-to-image (T2I) diffusion models (DMs) have shown promise in generating high-quality images from textual descriptions. The real-world applications of these models require particular attention to their safety and fidelity, but this has not been sufficiently explored. One fundamental question is whether existing T2I DMs are robust against variations over input texts. To answer it, this work provides the first robustness evaluation of T2I DMs against real-world attacks. Unlike prior studies that focus on malicious attacks involving apocryphal alterations to the input texts, we consider an att"},"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":"2306.13103","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2023-06-16T00:43:35Z","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"title_canon_sha256":"81e8a55c4c5af33df62115f41ccac1049065301867e757fdf6230d55b8d6293d","abstract_canon_sha256":"59b29491e0f6b1a2a485b81b5d919ba98d547783d8b4848140f9cb8a97e5a1f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:24:01.602004Z","signature_b64":"ur3xSuQoqjKmJMu/p/y/CX1gPphuueVCy8ScX1a/tVc4F4/LK420+YEZ800pZnschkDeEQZw48BtkT86rOkNAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78a70e17de03409b4a8abc7c89e83c096699fdb0ab125f65670017f1f754e6a7","last_reissued_at":"2026-07-05T06:24:01.601459Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:24:01.601459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV","cs.LG"],"primary_cat":"cs.CR","authors_text":"Hao Zhang, Hongcheng Gao, Yinpeng Dong, Zhijie Deng","submitted_at":"2023-06-16T00:43:35Z","abstract_excerpt":"Text-to-image (T2I) diffusion models (DMs) have shown promise in generating high-quality images from textual descriptions. The real-world applications of these models require particular attention to their safety and fidelity, but this has not been sufficiently explored. One fundamental question is whether existing T2I DMs are robust against variations over input texts. To answer it, this work provides the first robustness evaluation of T2I DMs against real-world attacks. Unlike prior studies that focus on malicious attacks involving apocryphal alterations to the input texts, we consider an att"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.13103","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/2306.13103/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":"2306.13103","created_at":"2026-07-05T06:24:01.601533+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.13103v1","created_at":"2026-07-05T06:24:01.601533+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.13103","created_at":"2026-07-05T06:24:01.601533+00:00"},{"alias_kind":"pith_short_12","alias_value":"PCTQ4F66ANAJ","created_at":"2026-07-05T06:24:01.601533+00:00"},{"alias_kind":"pith_short_16","alias_value":"PCTQ4F66ANAJWSUK","created_at":"2026-07-05T06:24:01.601533+00:00"},{"alias_kind":"pith_short_8","alias_value":"PCTQ4F66","created_at":"2026-07-05T06:24:01.601533+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09151","citing_title":"Customization under Fire: Plugin Poisoning in Text-to-Image Ecosystem","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF","json":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF.json","graph_json":"https://pith.science/api/pith-number/PCTQ4F66ANAJWSUKXR6IT2B4BF/graph.json","events_json":"https://pith.science/api/pith-number/PCTQ4F66ANAJWSUKXR6IT2B4BF/events.json","paper":"https://pith.science/paper/PCTQ4F66"},"agent_actions":{"view_html":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF","download_json":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF.json","view_paper":"https://pith.science/paper/PCTQ4F66","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.13103&json=true","fetch_graph":"https://pith.science/api/pith-number/PCTQ4F66ANAJWSUKXR6IT2B4BF/graph.json","fetch_events":"https://pith.science/api/pith-number/PCTQ4F66ANAJWSUKXR6IT2B4BF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF/action/storage_attestation","attest_author":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF/action/author_attestation","sign_citation":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF/action/citation_signature","submit_replication":"https://pith.science/pith/PCTQ4F66ANAJWSUKXR6IT2B4BF/action/replication_record"}},"created_at":"2026-07-05T06:24:01.601533+00:00","updated_at":"2026-07-05T06:24:01.601533+00:00"}