摘要
现有方法生成的音频分类对抗样本(adversarial example, AE)攻击成功率低,易被感知。鉴于此,设计了一种基于时频分区扰动(time-frequency partitioned perturbation, TFPP)的音频AE生成框架。音频信号的幅度谱根据时频特性被划分为关键和非关键区域,并生成相应的对抗扰动。在TFPP基础上,提出了一种基于生成对抗网络(generative adversarial network, GAN)的AE生成方法TFPPGAN,以分区幅度谱为输入,通过对抗训练自适应调整扰动约束系数,同时优化关键和非关键区域的扰动。3个典型音频分类数据集上的实验表明,与基线方法相比,TFPPGAN可将AE的攻击成功率、信噪比分别提高4.7%和5.5 dB,将生成的语音对抗样本的质量感知评价得分提高0.15。此外,理论分析了TFPP框架与其他攻击方法相结合的可行性,并通过实验验证了这种结合的有效性。
The adversarial examples generated by the existing methods generally suffer from a low attack success rate and are easy to perceive.To address these problems,this paper first designs an audio adversarial example generation framework based on Time-Frequency Partitioned Perturbation(TFPP).Leveraging the time-spectral characteristics of the audio signal,the framework divides the magnitude spectrum 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 Network(GAN)-based adversarial example generation method named TFPPGAN.TFPPGAN takes magnitude 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 coefficients.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 effectiveness of this combination.
作者
张雄伟
张强
杨吉斌
孙蒙
李毅豪
ZHANG Xiongwei;ZHANG Qiang;YANG Jibin;SUN Meng;LI Yihao(College of Command&Control Engineering,Army Engineering University of PLA,Nanjing 210007,China)
出处
《陆军工程大学学报》
2024年第1期1-11,共11页
Journal of Army Engineering University of PLA
基金
国家自然科学基金(62071484)。
关键词
音频分类
对抗样本
生成对抗网络
分区扰动
audio classification
adversarial example
generative adversarial network
partitioned perturbation
