{"total":31,"rows":[{"id":"2111.02840","title":"Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models","created":"2021-11-04","cited_by_count":232,"area":null},{"id":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127,"area":null},{"id":"2309.14700","title":"Structure Invariant Transformation for better Adversarial Transferability","created":"2023-09-26","cited_by_count":62,"area":null},{"id":"2103.03344","title":"WaveGuard: Understanding and Mitigating Audio Adversarial Examples","created":"2021-03-04","cited_by_count":40,"area":null},{"id":"2110.09714","title":"Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information","created":"2021-10-19","cited_by_count":25,"area":null},{"id":"2104.02000","title":"Can audio-visual integration strengthen robustness under multimodal attacks?","created":"2021-04-05","cited_by_count":23,"area":null},{"id":"2206.03393","title":"Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition","created":"2022-06-07","cited_by_count":23,"area":null},{"id":"2112.03000","title":"Sequential Randomized Smoothing for Adversarially Robust Speech Recognition","created":"2021-11-05","cited_by_count":13,"area":null},{"id":"2212.09006","title":"A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness","created":"2022-12-18","cited_by_count":13,"area":null},{"id":"2202.07201","title":"Holistic Adversarial Robustness of Deep Learning Models","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2102.05431","title":"Dompteur: Taming Audio Adversarial Examples","created":"2021-02-10","cited_by_count":10,"area":null},{"id":"2301.03052","title":"AI Maintenance: A Robustness Perspective","created":"2023-01-08","cited_by_count":6,"area":null},{"id":"2309.06960","title":"PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection","created":"2023-09-13","cited_by_count":5,"area":null},{"id":"2103.10651","title":"SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems","created":"2021-03-19","cited_by_count":4,"area":null},{"id":"2411.09220","title":"Transferable Adversarial Attacks against ASR","created":"2024-11-14","cited_by_count":4,"area":null},{"id":"2109.01766","title":"SEC4SR: A Security Analysis Platform for Speaker Recognition","created":"2021-09-04","cited_by_count":3,"area":null},{"id":"2202.05725","title":"On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems","created":"2022-02-11","cited_by_count":3,"area":null},{"id":"2305.17000","title":"DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution","created":"2023-05-26","cited_by_count":3,"area":null},{"id":"2202.08532","title":"Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition","created":"2022-02-17","cited_by_count":2,"area":null},{"id":"2209.13523","title":"Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models","created":"2022-09-17","cited_by_count":2,"area":null},{"id":"2401.12055","title":"NEUROSEC: FPGA-Based Neuromorphic Audio Security","created":"2024-01-22","cited_by_count":2,"area":null},{"id":"2405.09470","title":"Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer","created":"2024-05-15","cited_by_count":2,"area":null},{"id":"2504.00858","title":"Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems","created":"2025-04-01","cited_by_count":2,"area":null},{"id":"2112.01821","title":"Catch Me If You Can: Blackbox Adversarial Attacks on Automatic Speech Recognition using Frequency Masking","created":"2021-12-03","cited_by_count":1,"area":null},{"id":"2211.01621","title":"Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise","created":"2022-11-03","cited_by_count":1,"area":null},{"id":"2303.05758","title":"MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems","created":"2023-03-10","cited_by_count":1,"area":null},{"id":"2112.13060","title":"Fight Perturbations with Perturbations: Defending Adversarial Attacks via Neuron Influence","created":"2021-12-24","cited_by_count":0,"area":null},{"id":"2112.13144","title":"SoK: A Study of the Security on Voice Processing Systems","created":"2021-12-24","cited_by_count":0,"area":null},{"id":"2211.05446","title":"Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples","created":"2022-11-10","cited_by_count":0,"area":null},{"id":"2503.11627","title":"Are Deep Speech Denoising Models Robust to Adversarial Noise?","created":"2025-03-14","cited_by_count":0,"area":null},{"id":"2603.22590","title":"Precision-Varying Prediction (PVP): Robustifying ASR systems against adversarial attacks","created":"2026-03-23","cited_by_count":0,"area":null}],"next_cursor":null}