论文标题
反对端到端语音攻击的班级条件防御
Class-Conditional Defense GAN Against End-to-End Speech Attacks
论文作者
论文摘要
在本文中，我们提出了一种新颖的防御方法，以抵制终极的对抗攻击，以欺骗高级语音到文本系统，例如DeepSpeech和Lingvo。与常规的防御方法不同，所提出的方法不直接采用低级转换，例如自动编码给定的输入信号，旨在消除潜在的对抗性扰动。而不是这样，我们通过最大程度地减少给定的测试输入和生成器网络之间的相对串联距离调整，找到了类有条件生成对抗网络的最佳输入向量。然后，我们从合成的频谱图和从给定输入信号得出的原始相信息中重建1D信号。因此，这种重建并没有为信号增加任何额外的噪音，并且根据我们的实验结果，在单词错误率和句子级别识别精度方面，我们的防御能力大大优于常规的防御算法。
In this paper we propose a novel defense approach against end-to-end adversarial attacks developed to fool advanced speech-to-text systems such as DeepSpeech and Lingvo. Unlike conventional defense approaches, the proposed approach does not directly employ low-level transformations such as autoencoding a given input signal aiming at removing potential adversarial perturbation. Instead of that, we find an optimal input vector for a class conditional generative adversarial network through minimizing the relative chordal distance adjustment between a given test input and the generator network. Then, we reconstruct the 1D signal from the synthesized spectrogram and the original phase information derived from the given input signal. Hence, this reconstruction does not add any extra noise to the signal and according to our experimental results, our defense-GAN considerably outperforms conventional defense algorithms both in terms of word error rate and sentence level recognition accuracy.
