{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PJETBWGTV7RBDP6LEFQSPKB4ZD","short_pith_number":"pith:PJETBWGT","schema_version":"1.0","canonical_sha256":"7a4930d8d3afe211bfcb216127a83cc8f956a745886c765f8725218f9b4167eb","source":{"kind":"arxiv","id":"1903.11508","version":2},"attestation_state":"computed","paper":{"title":"Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","cs.LG"],"primary_cat":"cs.CL","authors_text":"Andreas R\\\"uckl\\'e, Claudia Schulz, Edwin Simpson, G\\\"ozde G\\\"ul \\c{S}ahin, Iryna Gurevych, Ji-Ung Lee, Krishnkant Swarnkar, Mohsen Mesgar, Steffen Eger","submitted_at":"2019-03-27T16:01:18Z","abstract_excerpt":"Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., \"!d10t\") or as a writing style (\"1337\" in \"leet speak\"), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual input perturbations demonstrate. We then investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensi"},"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":"1903.11508","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-03-27T16:01:18Z","cross_cats_sorted":["cs.CR","cs.CV","cs.LG"],"title_canon_sha256":"d18b7b76140cc0fa440c0dccda02f77b41a5a7f41a93e6935da2148070febd2f","abstract_canon_sha256":"8088812f75b3ff1da6faabb507b7bbf36db76a778cc430bf44af70b0515bb7e4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:08:59.138904Z","signature_b64":"EYo0pAhNEIyJw+V7RB8Rxqvec3KFGL8wyhVWksW+qaj3IWRGLYaB9Cm64bvX4ixKhCxH4I/1rlkLaP4EwQUyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a4930d8d3afe211bfcb216127a83cc8f956a745886c765f8725218f9b4167eb","last_reissued_at":"2026-07-05T01:08:59.138441Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:08:59.138441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV","cs.LG"],"primary_cat":"cs.CL","authors_text":"Andreas R\\\"uckl\\'e, Claudia Schulz, Edwin Simpson, G\\\"ozde G\\\"ul \\c{S}ahin, Iryna Gurevych, Ji-Ung Lee, Krishnkant Swarnkar, Mohsen Mesgar, Steffen Eger","submitted_at":"2019-03-27T16:01:18Z","abstract_excerpt":"Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., \"!d10t\") or as a writing style (\"1337\" in \"leet speak\"), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual input perturbations demonstrate. We then investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11508","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1903.11508/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":"1903.11508","created_at":"2026-07-05T01:08:59.138509+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.11508v2","created_at":"2026-07-05T01:08:59.138509+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11508","created_at":"2026-07-05T01:08:59.138509+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJETBWGTV7RB","created_at":"2026-07-05T01:08:59.138509+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJETBWGTV7RBDP6L","created_at":"2026-07-05T01:08:59.138509+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJETBWGT","created_at":"2026-07-05T01:08:59.138509+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09700","citing_title":"What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD","json":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD.json","graph_json":"https://pith.science/api/pith-number/PJETBWGTV7RBDP6LEFQSPKB4ZD/graph.json","events_json":"https://pith.science/api/pith-number/PJETBWGTV7RBDP6LEFQSPKB4ZD/events.json","paper":"https://pith.science/paper/PJETBWGT"},"agent_actions":{"view_html":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD","download_json":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD.json","view_paper":"https://pith.science/paper/PJETBWGT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.11508&json=true","fetch_graph":"https://pith.science/api/pith-number/PJETBWGTV7RBDP6LEFQSPKB4ZD/graph.json","fetch_events":"https://pith.science/api/pith-number/PJETBWGTV7RBDP6LEFQSPKB4ZD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD/action/storage_attestation","attest_author":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD/action/author_attestation","sign_citation":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD/action/citation_signature","submit_replication":"https://pith.science/pith/PJETBWGTV7RBDP6LEFQSPKB4ZD/action/replication_record"}},"created_at":"2026-07-05T01:08:59.138509+00:00","updated_at":"2026-07-05T01:08:59.138509+00:00"}