{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:JIM5UFWSOWBQMPC6FYHUCEBJA3","short_pith_number":"pith:JIM5UFWS","schema_version":"1.0","canonical_sha256":"4a19da16d27583063c5e2e0f41102906c0ee868be3829a6b356abed7bf8f39fe","source":{"kind":"arxiv","id":"2110.07139","version":1},"attestation_state":"computed","paper":{"title":"Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.CL","authors_text":"Fanchao Qi, Maosong Sun, Mukai Li, Xurui Zhang, Yangyi Chen, Zhiyuan Liu","submitted_at":"2021-10-14T03:54:16Z","abstract_excerpt":"Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning. Both of them harness task-irrelevant features of data in their implementation. Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitable for adversarial and backdoor attacks. In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning. We design an adversarial attack method and a backdoor attack method, and conduct extensive exp"},"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":"2110.07139","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-10-14T03:54:16Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"0e6227ea465db9db007f381b6380f1557ca4f30db0938730bbb43e69e3fa4b38","abstract_canon_sha256":"e1b1faab4da7b974a304b379dfdc6ad96ea8b2f5eb4f84296fcc77901030b4cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:22:39.231718Z","signature_b64":"fI7V6f8pTIfssFlQRAkC9GgJ+QSadCkLz1Z3Wra7ROiolzTnu+v2DS2mVHjzD3Un8HUMgGdISNE5zIOAhVp5Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a19da16d27583063c5e2e0f41102906c0ee868be3829a6b356abed7bf8f39fe","last_reissued_at":"2026-07-05T03:22:39.231306Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:22:39.231306Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.CL","authors_text":"Fanchao Qi, Maosong Sun, Mukai Li, Xurui Zhang, Yangyi Chen, Zhiyuan Liu","submitted_at":"2021-10-14T03:54:16Z","abstract_excerpt":"Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning. Both of them harness task-irrelevant features of data in their implementation. Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitable for adversarial and backdoor attacks. In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning. We design an adversarial attack method and a backdoor attack method, and conduct extensive exp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.07139","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/2110.07139/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":"2110.07139","created_at":"2026-07-05T03:22:39.231371+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.07139v1","created_at":"2026-07-05T03:22:39.231371+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.07139","created_at":"2026-07-05T03:22:39.231371+00:00"},{"alias_kind":"pith_short_12","alias_value":"JIM5UFWSOWBQ","created_at":"2026-07-05T03:22:39.231371+00:00"},{"alias_kind":"pith_short_16","alias_value":"JIM5UFWSOWBQMPC6","created_at":"2026-07-05T03:22:39.231371+00:00"},{"alias_kind":"pith_short_8","alias_value":"JIM5UFWS","created_at":"2026-07-05T03:22:39.231371+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09711","citing_title":"Proxy Reward Internalization and Mechanistic Exploitation: A Learned Precursor to Reward Hacking and Its Generalization","ref_index":72,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27631","citing_title":"Poison with Style: A Practical Poisoning Attack on Code Large Language Models","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2504.20984","citing_title":"ACE: A Security Architecture for LLM-Integrated App Systems","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2512.10998","citing_title":"SCOUT: A Defense Against Data Poisoning Attacks in Fine-Tuned Language Models","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3","json":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3.json","graph_json":"https://pith.science/api/pith-number/JIM5UFWSOWBQMPC6FYHUCEBJA3/graph.json","events_json":"https://pith.science/api/pith-number/JIM5UFWSOWBQMPC6FYHUCEBJA3/events.json","paper":"https://pith.science/paper/JIM5UFWS"},"agent_actions":{"view_html":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3","download_json":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3.json","view_paper":"https://pith.science/paper/JIM5UFWS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.07139&json=true","fetch_graph":"https://pith.science/api/pith-number/JIM5UFWSOWBQMPC6FYHUCEBJA3/graph.json","fetch_events":"https://pith.science/api/pith-number/JIM5UFWSOWBQMPC6FYHUCEBJA3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3/action/storage_attestation","attest_author":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3/action/author_attestation","sign_citation":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3/action/citation_signature","submit_replication":"https://pith.science/pith/JIM5UFWSOWBQMPC6FYHUCEBJA3/action/replication_record"}},"created_at":"2026-07-05T03:22:39.231371+00:00","updated_at":"2026-07-05T03:22:39.231371+00:00"}