{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZRRXJTT2BOSOASPBSGTWD5TRWP","short_pith_number":"pith:ZRRXJTT2","canonical_record":{"source":{"id":"1801.04354","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-13T00:42:30Z","cross_cats_sorted":["cs.CR","cs.IR","cs.LG"],"title_canon_sha256":"2769e3bf440db9c5562822c29001043a70b67405131aedb5973306a9e666f57d","abstract_canon_sha256":"3eb6890d6a98e98b77ba1bea5b65cb3888952f824ec820199bf72f3d405f807a"},"schema_version":"1.0"},"canonical_sha256":"cc6374ce7a0ba4e049e191a761f671b3edf65981e17b2b5c446ba3a7f903121d","source":{"kind":"arxiv","id":"1801.04354","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.04354","created_at":"2026-05-18T00:15:09Z"},{"alias_kind":"arxiv_version","alias_value":"1801.04354v5","created_at":"2026-05-18T00:15:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.04354","created_at":"2026-05-18T00:15:09Z"},{"alias_kind":"pith_short_12","alias_value":"ZRRXJTT2BOSO","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZRRXJTT2BOSOASPB","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZRRXJTT2","created_at":"2026-05-18T12:33:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZRRXJTT2BOSOASPBSGTWD5TRWP","target":"record","payload":{"canonical_record":{"source":{"id":"1801.04354","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-13T00:42:30Z","cross_cats_sorted":["cs.CR","cs.IR","cs.LG"],"title_canon_sha256":"2769e3bf440db9c5562822c29001043a70b67405131aedb5973306a9e666f57d","abstract_canon_sha256":"3eb6890d6a98e98b77ba1bea5b65cb3888952f824ec820199bf72f3d405f807a"},"schema_version":"1.0"},"canonical_sha256":"cc6374ce7a0ba4e049e191a761f671b3edf65981e17b2b5c446ba3a7f903121d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:09.828784Z","signature_b64":"U20jd+vOhZwjReVsJGM8Pm5IR4fpECAUkpXdw1RLICSxM7A68P1HblC+RoX9U1DEn58bYTzcS035aGXyaDWMDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc6374ce7a0ba4e049e191a761f671b3edf65981e17b2b5c446ba3a7f903121d","last_reissued_at":"2026-05-18T00:15:09.828134Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:09.828134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.04354","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:15:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LDP+bKQLzdiasbehQsU9EEPMcAfI9HH31sBKzxpC9JIOAMoQ9wWRLUZH8hs8VPfkSjfKFIXWcTyLcR+KHSdhAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T18:22:22.892928Z"},"content_sha256":"9f1ebfba6c8323f61f39d366bc3f1bc23573a0b983ebd95550a007e2c17b89fa","schema_version":"1.0","event_id":"sha256:9f1ebfba6c8323f61f39d366bc3f1bc23573a0b983ebd95550a007e2c17b89fa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZRRXJTT2BOSOASPBSGTWD5TRWP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Jack Lanchantin, Ji Gao, Mary Lou Soffa, Yanjun Qi","submitted_at":"2018-01-13T00:42:30Z","abstract_excerpt":"Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We employ novel scoring strategies to identify the critical tokens that, if modified, cause the classifier to make an incorrect prediction. Simple character-level transformations are applied to the highe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.04354","kind":"arxiv","version":5},"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":"2026-05-18T00:15:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3RWj9zH786jsSEi7aBKRi3y+leGJkNkLx056ktWpOGq8yZsfFDssDF6JTZaBayCSyZp8alJsOHPSAwwmNIQEBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T18:22:22.893671Z"},"content_sha256":"ccee8fa23d70f132d3e1fd86893764cf19af8a88a64e833bb53c1b1289b6c2b4","schema_version":"1.0","event_id":"sha256:ccee8fa23d70f132d3e1fd86893764cf19af8a88a64e833bb53c1b1289b6c2b4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZRRXJTT2BOSOASPBSGTWD5TRWP/bundle.json","state_url":"https://pith.science/pith/ZRRXJTT2BOSOASPBSGTWD5TRWP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZRRXJTT2BOSOASPBSGTWD5TRWP/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":"2026-05-28T18:22:22Z","links":{"resolver":"https://pith.science/pith/ZRRXJTT2BOSOASPBSGTWD5TRWP","bundle":"https://pith.science/pith/ZRRXJTT2BOSOASPBSGTWD5TRWP/bundle.json","state":"https://pith.science/pith/ZRRXJTT2BOSOASPBSGTWD5TRWP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZRRXJTT2BOSOASPBSGTWD5TRWP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZRRXJTT2BOSOASPBSGTWD5TRWP","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":"3eb6890d6a98e98b77ba1bea5b65cb3888952f824ec820199bf72f3d405f807a","cross_cats_sorted":["cs.CR","cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-13T00:42:30Z","title_canon_sha256":"2769e3bf440db9c5562822c29001043a70b67405131aedb5973306a9e666f57d"},"schema_version":"1.0","source":{"id":"1801.04354","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.04354","created_at":"2026-05-18T00:15:09Z"},{"alias_kind":"arxiv_version","alias_value":"1801.04354v5","created_at":"2026-05-18T00:15:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.04354","created_at":"2026-05-18T00:15:09Z"},{"alias_kind":"pith_short_12","alias_value":"ZRRXJTT2BOSO","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_16","alias_value":"ZRRXJTT2BOSOASPB","created_at":"2026-05-18T12:33:07Z"},{"alias_kind":"pith_short_8","alias_value":"ZRRXJTT2","created_at":"2026-05-18T12:33:07Z"}],"graph_snapshots":[{"event_id":"sha256:ccee8fa23d70f132d3e1fd86893764cf19af8a88a64e833bb53c1b1289b6c2b4","target":"graph","created_at":"2026-05-18T00:15:09Z","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":"Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We employ novel scoring strategies to identify the critical tokens that, if modified, cause the classifier to make an incorrect prediction. Simple character-level transformations are applied to the highe","authors_text":"Jack Lanchantin, Ji Gao, Mary Lou Soffa, Yanjun Qi","cross_cats":["cs.CR","cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-13T00:42:30Z","title":"Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.04354","kind":"arxiv","version":5},"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":"sha256:9f1ebfba6c8323f61f39d366bc3f1bc23573a0b983ebd95550a007e2c17b89fa","target":"record","created_at":"2026-05-18T00:15:09Z","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":"3eb6890d6a98e98b77ba1bea5b65cb3888952f824ec820199bf72f3d405f807a","cross_cats_sorted":["cs.CR","cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-13T00:42:30Z","title_canon_sha256":"2769e3bf440db9c5562822c29001043a70b67405131aedb5973306a9e666f57d"},"schema_version":"1.0","source":{"id":"1801.04354","kind":"arxiv","version":5}},"canonical_sha256":"cc6374ce7a0ba4e049e191a761f671b3edf65981e17b2b5c446ba3a7f903121d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc6374ce7a0ba4e049e191a761f671b3edf65981e17b2b5c446ba3a7f903121d","first_computed_at":"2026-05-18T00:15:09.828134Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:09.828134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U20jd+vOhZwjReVsJGM8Pm5IR4fpECAUkpXdw1RLICSxM7A68P1HblC+RoX9U1DEn58bYTzcS035aGXyaDWMDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:09.828784Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.04354","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9f1ebfba6c8323f61f39d366bc3f1bc23573a0b983ebd95550a007e2c17b89fa","sha256:ccee8fa23d70f132d3e1fd86893764cf19af8a88a64e833bb53c1b1289b6c2b4"],"state_sha256":"f8ad79df5bd6e22ed78048f53111aa65d03092895e4df4556d5b37dabb9257fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"h8TufVDsQ0yoEgSagMWeKr6GJ3J7CqwsvzjKOHEpoYUyJVyDROdBdM7KV1KtqQ0XTMHCPKkpS9COo1ulRS1OCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T18:22:22.897565Z","bundle_sha256":"e5600c5173a0a2f4b562e5f3b521ac1cf9a4af33e87ca6353d6dbe4d9616a3f5"}}