{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:PJQD3JB47T6ICCEFWVR4J4UE5O","short_pith_number":"pith:PJQD3JB4","canonical_record":{"source":{"id":"1902.07285","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-02-12T02:42:54Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"819f09d42dd46266a595973a4751058027c32ac6cf2f30f0205dd7cfb5de190e","abstract_canon_sha256":"bd6da919698f69b7b76ebc6ab0aeb53a2a492640ec2ff5d7cefc5df8f69f46e0"},"schema_version":"1.0"},"canonical_sha256":"7a603da43cfcfc810885b563c4f284eb9b29ac3885fa25c741ecf77f6fc99daa","source":{"kind":"arxiv","id":"1902.07285","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.07285","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"arxiv_version","alias_value":"1902.07285v6","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.07285","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"pith_short_12","alias_value":"PJQD3JB47T6I","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"pith_short_16","alias_value":"PJQD3JB47T6ICCEF","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"pith_short_8","alias_value":"PJQD3JB4","created_at":"2026-07-05T02:33:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:PJQD3JB47T6ICCEFWVR4J4UE5O","target":"record","payload":{"canonical_record":{"source":{"id":"1902.07285","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-02-12T02:42:54Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"819f09d42dd46266a595973a4751058027c32ac6cf2f30f0205dd7cfb5de190e","abstract_canon_sha256":"bd6da919698f69b7b76ebc6ab0aeb53a2a492640ec2ff5d7cefc5df8f69f46e0"},"schema_version":"1.0"},"canonical_sha256":"7a603da43cfcfc810885b563c4f284eb9b29ac3885fa25c741ecf77f6fc99daa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:33:52.575535Z","signature_b64":"PIgh4Kcp9wQPoh9bbgALzMgiuAeOAu+t/2rpLAC3Pt4rJglgDxZt5Ez8OVIl28HaGS0pfS3odzWSIXdvRv18Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a603da43cfcfc810885b563c4f284eb9b29ac3885fa25c741ecf77f6fc99daa","last_reissued_at":"2026-07-05T02:33:52.575178Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:33:52.575178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.07285","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:33:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"msjDkP7PsW9kaJ4YE5t5KptvSHtIElGXX3lAumg0igymQJflrBTHUY6FATYp9+hcCrQp/GsXVMx3z0EjnWA9CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T01:20:42.407274Z"},"content_sha256":"0f36735ec28f84d9df60996db60c969086d5e8eedea78568e9a018394aa998f8","schema_version":"1.0","event_id":"sha256:0f36735ec28f84d9df60996db60c969086d5e8eedea78568e9a018394aa998f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:PJQD3JB47T6ICCEFWVR4J4UE5O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Towards a Robust Deep Neural Network in Texts: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aoshuang Ye, Lina Wang, Run Wang, Wenqi Wang, Zhibo Wang","submitted_at":"2019-02-12T02:42:54Z","abstract_excerpt":"Deep neural networks (DNNs) have achieved remarkable success in various tasks (e.g., image classification, speech recognition, and natural language processing (NLP)). However, researchers have demonstrated that DNN-based models are vulnerable to adversarial examples, which cause erroneous predictions by adding imperceptible perturbations into legitimate inputs. Recently, studies have revealed adversarial examples in the text domain, which could effectively evade various DNN-based text analyzers and further bring the threats of the proliferation of disinformation. In this paper, we give a compr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.07285","kind":"arxiv","version":6},"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/1902.07285/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:33:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sJ3Yj8Mb5AUYQ7rxicVk+8xZIC7eUszr7dfRo7jfQ6Ll7Q+zXIfvF7x5oHrOPSt4O3laanNIqfW/fnHkt6S5Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T01:20:42.407849Z"},"content_sha256":"6e11782198e2154fcb09ce6403a26bc27cac465c10a390b03c4075fe59d2d333","schema_version":"1.0","event_id":"sha256:6e11782198e2154fcb09ce6403a26bc27cac465c10a390b03c4075fe59d2d333"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PJQD3JB47T6ICCEFWVR4J4UE5O/bundle.json","state_url":"https://pith.science/pith/PJQD3JB47T6ICCEFWVR4J4UE5O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PJQD3JB47T6ICCEFWVR4J4UE5O/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":"2026-08-08T01:20:42Z","links":{"resolver":"https://pith.science/pith/PJQD3JB47T6ICCEFWVR4J4UE5O","bundle":"https://pith.science/pith/PJQD3JB47T6ICCEFWVR4J4UE5O/bundle.json","state":"https://pith.science/pith/PJQD3JB47T6ICCEFWVR4J4UE5O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PJQD3JB47T6ICCEFWVR4J4UE5O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:PJQD3JB47T6ICCEFWVR4J4UE5O","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":"bd6da919698f69b7b76ebc6ab0aeb53a2a492640ec2ff5d7cefc5df8f69f46e0","cross_cats_sorted":["cs.CR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-02-12T02:42:54Z","title_canon_sha256":"819f09d42dd46266a595973a4751058027c32ac6cf2f30f0205dd7cfb5de190e"},"schema_version":"1.0","source":{"id":"1902.07285","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.07285","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"arxiv_version","alias_value":"1902.07285v6","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.07285","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"pith_short_12","alias_value":"PJQD3JB47T6I","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"pith_short_16","alias_value":"PJQD3JB47T6ICCEF","created_at":"2026-07-05T02:33:52Z"},{"alias_kind":"pith_short_8","alias_value":"PJQD3JB4","created_at":"2026-07-05T02:33:52Z"}],"graph_snapshots":[{"event_id":"sha256:6e11782198e2154fcb09ce6403a26bc27cac465c10a390b03c4075fe59d2d333","target":"graph","created_at":"2026-07-05T02:33:52Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1902.07285/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural networks (DNNs) have achieved remarkable success in various tasks (e.g., image classification, speech recognition, and natural language processing (NLP)). However, researchers have demonstrated that DNN-based models are vulnerable to adversarial examples, which cause erroneous predictions by adding imperceptible perturbations into legitimate inputs. Recently, studies have revealed adversarial examples in the text domain, which could effectively evade various DNN-based text analyzers and further bring the threats of the proliferation of disinformation. In this paper, we give a compr","authors_text":"Aoshuang Ye, Lina Wang, Run Wang, Wenqi Wang, Zhibo Wang","cross_cats":["cs.CR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-02-12T02:42:54Z","title":"Towards a Robust Deep Neural Network in Texts: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.07285","kind":"arxiv","version":6},"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":"sha256:0f36735ec28f84d9df60996db60c969086d5e8eedea78568e9a018394aa998f8","target":"record","created_at":"2026-07-05T02:33:52Z","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":"bd6da919698f69b7b76ebc6ab0aeb53a2a492640ec2ff5d7cefc5df8f69f46e0","cross_cats_sorted":["cs.CR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-02-12T02:42:54Z","title_canon_sha256":"819f09d42dd46266a595973a4751058027c32ac6cf2f30f0205dd7cfb5de190e"},"schema_version":"1.0","source":{"id":"1902.07285","kind":"arxiv","version":6}},"canonical_sha256":"7a603da43cfcfc810885b563c4f284eb9b29ac3885fa25c741ecf77f6fc99daa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7a603da43cfcfc810885b563c4f284eb9b29ac3885fa25c741ecf77f6fc99daa","first_computed_at":"2026-07-05T02:33:52.575178Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:33:52.575178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PIgh4Kcp9wQPoh9bbgALzMgiuAeOAu+t/2rpLAC3Pt4rJglgDxZt5Ez8OVIl28HaGS0pfS3odzWSIXdvRv18Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:33:52.575535Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.07285","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0f36735ec28f84d9df60996db60c969086d5e8eedea78568e9a018394aa998f8","sha256:6e11782198e2154fcb09ce6403a26bc27cac465c10a390b03c4075fe59d2d333"],"state_sha256":"82179f8391cafe93050b8c1b73ffafb9616897830e6c5d1f94ab2ec8c0878166"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JoDZhhRI4LlQEeQ/eH2aBWG6ZidMdOecBgSm5PobUYuI7cw8nb8Pn4awIJ+TUvoX4tHdx3TVF3nWj1JUomDyCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T01:20:42.411430Z","bundle_sha256":"0bafa52f47cad69969cf6faffac8ed212e82ad1256edb6c5b032ac622e3fe747"}}