Adversarial Example Generation for Audio Classification Based on Time-Frequency Partitioned Perturbation
The adversarial examples generated by the existing methods generally suffer from a low at-tack success rate and are easy to perceive.To address these problems,this paper first designs an audio ad-versarial example generation framework based on Time-Frequency Partitioned Perturbation(TFPP).Le-veraging the time-spectral characteristics of the audio signal,the framework divides the magnitude spec-trum of the input audio signal into critical regions and non-critical regions,and generates the corresponding perturbations.Building upon this framework,this paper further proposes a Generative Adversarial Net-work(GAN)-based adversarial example generation method named TFPPGAN.TFPPGAN takes magni-tude spectra as inputs and uses adversarial training to simultaneously optimize the adversarial perturbations in critical and non-critical regions by adaptively adjusting the partitioned perturbation constraint coeffi-cients.Exhaustive comparison experiments are conducted on three typical audio classification datasets.The experimental results show that,compared with baseline methods,TFPPGAN can improve the attack success rate and signal-to-noise ratio by 4.7%and 5.5 dB respectively.The perceptual evaluation score of generated adversarial speech quality also improves by 0.15.Besides,this paper theoretically analyzes the feasibility of the combination of TFPP with other attack methods,and experimentally verify the effective-ness of this combination.
NETL
NSTL
万方数据
维普
