Targeted Adversarial Examples for Black Box Audio Systems

Authors: Rohan Taori, Amog Kamsetty, Brenton Chu, Nikita Vemuri
Published: 2018-05-20 | Updated: 2019-08-20

Source: https://arxiv.org/abs/1805.07820

PDF: https://arxiv.org/pdf/1805.07820

Labels Predicted by AI

音声認識システム モデルの頑健性保証 敵対的攻撃検出

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Abstract

The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25 94.6