{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:LUBEHXTU7DD7OAVAVZBKI665AG","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":"ea1db79344f25491b7c6690918f6b7973d196d9def680b548112fc21f9f27cb9","cross_cats_sorted":["cs.AI","cs.LG","cs.NE","cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-02-17T09:17:58Z","title_canon_sha256":"50c9d262c8f472a08062d00b4ee141e973251450d0de31cf8ee2b687b3d57662"},"schema_version":"1.0","source":{"id":"2202.08532","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.08532","created_at":"2026-07-05T03:57:47Z"},{"alias_kind":"arxiv_version","alias_value":"2202.08532v1","created_at":"2026-07-05T03:57:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.08532","created_at":"2026-07-05T03:57:47Z"},{"alias_kind":"pith_short_12","alias_value":"LUBEHXTU7DD7","created_at":"2026-07-05T03:57:47Z"},{"alias_kind":"pith_short_16","alias_value":"LUBEHXTU7DD7OAVA","created_at":"2026-07-05T03:57:47Z"},{"alias_kind":"pith_short_8","alias_value":"LUBEHXTU","created_at":"2026-07-05T03:57:47Z"}],"graph_snapshots":[{"event_id":"sha256:ecb0dfa43d4f37790906e4eb4392637444ff52bc461b1242761f133603a8f0fb","target":"graph","created_at":"2026-07-05T03:57:47Z","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/2202.08532/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we aim to enhance the system robustness of end-to-end automatic speech recognition (ASR) against adversarially-noisy speech examples. We focus on a rigorous and empirical \"closed-model adversarial robustness\" setting (e.g., on-device or cloud applications). The adversarial noise is only generated by closed-model optimization (e.g., evolutionary and zeroth-order estimation) without accessing gradient information of a targeted ASR model directly. We propose an advanced Bayesian neural network (BNN) based adversarial detector, which could model latent distributions against adaptive ","authors_text":"Andreas Stolcke, Chao-Han Huck Yang, Ivan Bulyko, Joseph Szurley, Linda Liu, Roger Ren, Yile Gu, Zeeshan Ahmed","cross_cats":["cs.AI","cs.LG","cs.NE","cs.SD"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-02-17T09:17:58Z","title":"Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.08532","kind":"arxiv","version":1},"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":"sha256:9e1194e785e0e27fa0e85ce07059a0e7cb17d9fa358bc68f0286a4f74552e29a","target":"record","created_at":"2026-07-05T03:57:47Z","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":"ea1db79344f25491b7c6690918f6b7973d196d9def680b548112fc21f9f27cb9","cross_cats_sorted":["cs.AI","cs.LG","cs.NE","cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-02-17T09:17:58Z","title_canon_sha256":"50c9d262c8f472a08062d00b4ee141e973251450d0de31cf8ee2b687b3d57662"},"schema_version":"1.0","source":{"id":"2202.08532","kind":"arxiv","version":1}},"canonical_sha256":"5d0243de74f8c7f702a0ae42a47bdd019aef54f9114f770e8cc97f844996893d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d0243de74f8c7f702a0ae42a47bdd019aef54f9114f770e8cc97f844996893d","first_computed_at":"2026-07-05T03:57:47.096645Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:57:47.096645Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/4Rak6pYf1DiFEj9S3S0dK1+6GcUlxdoB1tEwqPrr9eiXZb46SA9VFeKtaFTrJVgfUyrbN+WcaMP9sBjjOWjCg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:57:47.097086Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.08532","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9e1194e785e0e27fa0e85ce07059a0e7cb17d9fa358bc68f0286a4f74552e29a","sha256:ecb0dfa43d4f37790906e4eb4392637444ff52bc461b1242761f133603a8f0fb"],"state_sha256":"f84a64651223967c740bfe059d7d2e7a1b7e0163b85a054d7b668fa0f2832d1b"}