{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VBYQDTRMQU7BSLKVWGH2JITEMK","short_pith_number":"pith:VBYQDTRM","canonical_record":{"source":{"id":"1712.06751","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-19T02:15:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8163a56ecaf719d1bf592bcc823cc50f365dfdb5179d8a0c3f097e1d56844243","abstract_canon_sha256":"2dd2c489ce380d3123e974b565ac1573ad9826f42dd7c12cd6c297e7dbb93e0f"},"schema_version":"1.0"},"canonical_sha256":"a87101ce2c853e192d55b18fa4a264629f49ff90e324da18d8194535dda27ceb","source":{"kind":"arxiv","id":"1712.06751","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.06751","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"arxiv_version","alias_value":"1712.06751v2","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.06751","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"pith_short_12","alias_value":"VBYQDTRMQU7B","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VBYQDTRMQU7BSLKV","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VBYQDTRM","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VBYQDTRMQU7BSLKVWGH2JITEMK","target":"record","payload":{"canonical_record":{"source":{"id":"1712.06751","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-19T02:15:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8163a56ecaf719d1bf592bcc823cc50f365dfdb5179d8a0c3f097e1d56844243","abstract_canon_sha256":"2dd2c489ce380d3123e974b565ac1573ad9826f42dd7c12cd6c297e7dbb93e0f"},"schema_version":"1.0"},"canonical_sha256":"a87101ce2c853e192d55b18fa4a264629f49ff90e324da18d8194535dda27ceb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:05.144496Z","signature_b64":"RqqqelO1j9mbpop9OnYpjHKHyS+YgpPvACj41TAbmcCqqs03FytsuxLHZcDvSm0PEBXJH5rz3mE8n0cYQbc4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a87101ce2c853e192d55b18fa4a264629f49ff90e324da18d8194535dda27ceb","last_reissued_at":"2026-05-18T00:15:05.143745Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:05.143745Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.06751","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HtAsjib1a5o2034nACBUUgaQF9UjlevV0IlVMrBq14pvpvwAo+uEzlf+fW/kYygI6y1fb8Tahq4XKhVEj2kMAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:38:53.271764Z"},"content_sha256":"5eb67d0df95243db35d8062f62c02ee13f04adbb66921b32eb87a0fac481715b","schema_version":"1.0","event_id":"sha256:5eb67d0df95243db35d8062f62c02ee13f04adbb66921b32eb87a0fac481715b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VBYQDTRMQU7BSLKVWGH2JITEMK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"HotFlip: White-Box Adversarial Examples for Text Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Anyi Rao, Daniel Lowd, Dejing Dou, Javid Ebrahimi","submitted_at":"2017-12-19T02:15:19Z","abstract_excerpt":"We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.06751","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zNYK3cXfRxWckUPOzfAwj1g5sZGF1+buDeetgPKwS6KCQ3kBxMLoDvjBOXrQSLTpKPfIBCjiC9/NYmK6ZW+2Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T00:38:53.272124Z"},"content_sha256":"6c9b407954fd7b979f9645161f5a28026de71f05bbfebf8616cd29aec5f635a9","schema_version":"1.0","event_id":"sha256:6c9b407954fd7b979f9645161f5a28026de71f05bbfebf8616cd29aec5f635a9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VBYQDTRMQU7BSLKVWGH2JITEMK/bundle.json","state_url":"https://pith.science/pith/VBYQDTRMQU7BSLKVWGH2JITEMK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VBYQDTRMQU7BSLKVWGH2JITEMK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-27T00:38:53Z","links":{"resolver":"https://pith.science/pith/VBYQDTRMQU7BSLKVWGH2JITEMK","bundle":"https://pith.science/pith/VBYQDTRMQU7BSLKVWGH2JITEMK/bundle.json","state":"https://pith.science/pith/VBYQDTRMQU7BSLKVWGH2JITEMK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VBYQDTRMQU7BSLKVWGH2JITEMK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VBYQDTRMQU7BSLKVWGH2JITEMK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2dd2c489ce380d3123e974b565ac1573ad9826f42dd7c12cd6c297e7dbb93e0f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-19T02:15:19Z","title_canon_sha256":"8163a56ecaf719d1bf592bcc823cc50f365dfdb5179d8a0c3f097e1d56844243"},"schema_version":"1.0","source":{"id":"1712.06751","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.06751","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"arxiv_version","alias_value":"1712.06751v2","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.06751","created_at":"2026-05-18T00:15:05Z"},{"alias_kind":"pith_short_12","alias_value":"VBYQDTRMQU7B","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VBYQDTRMQU7BSLKV","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VBYQDTRM","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:6c9b407954fd7b979f9645161f5a28026de71f05bbfebf8616cd29aec5f635a9","target":"graph","created_at":"2026-05-18T00:15:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We propose an efficient method to generate white-box adversarial examples to trick a character-level neural classifier. We find that only a few manipulations are needed to greatly decrease the accuracy. Our method relies on an atomic flip operation, which swaps one token for another, based on the gradients of the one-hot input vectors. Due to efficiency of our method, we can perform adversarial training which makes the model more robust to attacks at test time. With the use of a few semantics-preserving constraints, we demonstrate that HotFlip can be adapted to attack a word-level classifier a","authors_text":"Anyi Rao, Daniel Lowd, Dejing Dou, Javid Ebrahimi","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-19T02:15:19Z","title":"HotFlip: White-Box Adversarial Examples for Text Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.06751","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5eb67d0df95243db35d8062f62c02ee13f04adbb66921b32eb87a0fac481715b","target":"record","created_at":"2026-05-18T00:15:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2dd2c489ce380d3123e974b565ac1573ad9826f42dd7c12cd6c297e7dbb93e0f","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-19T02:15:19Z","title_canon_sha256":"8163a56ecaf719d1bf592bcc823cc50f365dfdb5179d8a0c3f097e1d56844243"},"schema_version":"1.0","source":{"id":"1712.06751","kind":"arxiv","version":2}},"canonical_sha256":"a87101ce2c853e192d55b18fa4a264629f49ff90e324da18d8194535dda27ceb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a87101ce2c853e192d55b18fa4a264629f49ff90e324da18d8194535dda27ceb","first_computed_at":"2026-05-18T00:15:05.143745Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:05.143745Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RqqqelO1j9mbpop9OnYpjHKHyS+YgpPvACj41TAbmcCqqs03FytsuxLHZcDvSm0PEBXJH5rz3mE8n0cYQbc4BQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:05.144496Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.06751","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5eb67d0df95243db35d8062f62c02ee13f04adbb66921b32eb87a0fac481715b","sha256:6c9b407954fd7b979f9645161f5a28026de71f05bbfebf8616cd29aec5f635a9"],"state_sha256":"2b6ab251c8c76b20830ae0405933a36b7f34de225b4a50eedbd07023bcb77605"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ikIAHbc5vMsVu/fyrMu5s5JpUfaekNGU3v0EUiQ/H9VQTSzANYXRJOrQJLITzu+5BUkNoEdb7SKyqfUdM2quBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T00:38:53.274917Z","bundle_sha256":"e515e761bc9b6ca50d05ea77cbf76761fc4814289f97b2499eb36195d416a451"}}