2018/01/02 by Moustafa Alzantot, Bharathan Balaji, Alzantot, Moustafa +3 · 1 voice · 198 citations
Computer Science · #Adversarial Robustness in Machine Learning #Adversarial system #Anomaly Detection Techniques and Applications #Architecture #Artificial intelligence #Computation and Language (cs.CL) #Computer science #Cryptography and Security (cs.CR) #Deep learning #FOS: Computer and information sciences #Image (mathematics) #Machine learning #Noise (video) #Object (grammar) #Perception #Speech processing #Speech recognition #Voice activity detection #cs.CL #cs.CR
paper · pdf · doi:10.48550/arxiv.1801.00554
published in arXiv (Cornell University) (Cornell University) · Published in NIPS 2017 Machine Deception workshop
arxiv created 2018/01/02 · openalex publication_date 2018/01/02 · arxiv published 2018/01/02 · arxiv updated 2018/01/03 · openalex created_date 2018/01/12 · openalex updated_date 2026/07/28
Speech is a common and effective way of communication between humans, and modern consumer devices such as smartphones and home hubs are equipped with deep learning based accurate automatic speech recognition to enable natural interaction between humans and machines. Recently, researchers have demonstrated powerful attacks against machine learning models that can fool them to produceincorrect results. However, nearly all previous research in adversarial attacks has focused on image recognition and object detection models. In this short paper, we present a first of its kind demonstration of adversarial attacks against speech classification model. Our algorithm performs targeted attacks with 87% success by adding small background noise without having to know the underlying model parameter and architecture. Our attack only changes the least significant bits of a subset of audio clip samples, and the noise does not change 89% the human listener's perception of the audio clip as evaluated in our human study.