{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Y7ZMAWJY54MIYMEXXYJFWBP72Q","short_pith_number":"pith:Y7ZMAWJY","schema_version":"1.0","canonical_sha256":"c7f2c05938ef188c3097be125b05ffd40ba544a697543db025cf23b449c3c110","source":{"kind":"arxiv","id":"2402.07347","version":1},"attestation_state":"computed","paper":{"title":"Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Usman Roshan, Yunzhe Xue","submitted_at":"2024-02-12T00:36:34Z","abstract_excerpt":"Recent work has shown the defense of 01 loss sign activation neural networks against image classification adversarial attacks. A public challenge to attack the models on CIFAR10 dataset remains undefeated. We ask the following question in this study: are 01 loss sign activation neural networks hard to deceive with a popular black box text adversarial attack program called TextFooler? We study this question on four popular text classification datasets: IMDB reviews, Yelp reviews, MR sentiment classification, and AG news classification. We find that our 01 loss sign activation network is much ha"},"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":"2402.07347","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-12T00:36:34Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"609336be1586ad135ad0f11d143f7d95a664b706c2934c56778e02a0c304647b","abstract_canon_sha256":"6a7e7d8423761ee87c206aaf97b2c14e81af2c97bde64bc8ef3cf17d768641d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:44:04.924048Z","signature_b64":"Gy4y9DL8JZkcaNuMjS84edkIy9LV8JP2aCXVN9iLa5cMGLjaKkZTg8iV+E/OVY0XNxQmfzvZGpUKquwFxYScCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c7f2c05938ef188c3097be125b05ffd40ba544a697543db025cf23b449c3c110","last_reissued_at":"2026-07-05T07:44:04.923593Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:44:04.923593Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Usman Roshan, Yunzhe Xue","submitted_at":"2024-02-12T00:36:34Z","abstract_excerpt":"Recent work has shown the defense of 01 loss sign activation neural networks against image classification adversarial attacks. A public challenge to attack the models on CIFAR10 dataset remains undefeated. We ask the following question in this study: are 01 loss sign activation neural networks hard to deceive with a popular black box text adversarial attack program called TextFooler? We study this question on four popular text classification datasets: IMDB reviews, Yelp reviews, MR sentiment classification, and AG news classification. We find that our 01 loss sign activation network is much ha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.07347","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/2402.07347/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":"2402.07347","created_at":"2026-07-05T07:44:04.923655+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.07347v1","created_at":"2026-07-05T07:44:04.923655+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.07347","created_at":"2026-07-05T07:44:04.923655+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y7ZMAWJY54MI","created_at":"2026-07-05T07:44:04.923655+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y7ZMAWJY54MIYMEX","created_at":"2026-07-05T07:44:04.923655+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y7ZMAWJY","created_at":"2026-07-05T07:44:04.923655+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.21858","citing_title":"Low-Cost Test-Time Adaptation for Robust Video Editing","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q","json":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q.json","graph_json":"https://pith.science/api/pith-number/Y7ZMAWJY54MIYMEXXYJFWBP72Q/graph.json","events_json":"https://pith.science/api/pith-number/Y7ZMAWJY54MIYMEXXYJFWBP72Q/events.json","paper":"https://pith.science/paper/Y7ZMAWJY"},"agent_actions":{"view_html":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q","download_json":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q.json","view_paper":"https://pith.science/paper/Y7ZMAWJY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.07347&json=true","fetch_graph":"https://pith.science/api/pith-number/Y7ZMAWJY54MIYMEXXYJFWBP72Q/graph.json","fetch_events":"https://pith.science/api/pith-number/Y7ZMAWJY54MIYMEXXYJFWBP72Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q/action/storage_attestation","attest_author":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q/action/author_attestation","sign_citation":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q/action/citation_signature","submit_replication":"https://pith.science/pith/Y7ZMAWJY54MIYMEXXYJFWBP72Q/action/replication_record"}},"created_at":"2026-07-05T07:44:04.923655+00:00","updated_at":"2026-07-05T07:44:04.923655+00:00"}