Malicious actors may seek to use different voice-spoofing attacks to fool ASV systems and even use them for spreading misinformation. Various countermeasures have been proposed to detect these spoofing attacks. Due to the extensive work done on spoofing detection in automated speaker verification (ASV) systems in the last 6-7 years, there is a need to classify the research and perform qualitative and quantitative comparisons on state-of-the-art countermeasures. Additionally, no existing survey paper has reviewed integrated solutions to voice spoofing evaluation and speaker verification, adversarial/antiforensics attacks on spoofing countermeasures, and ASV itself, or unified solutions to detect multiple attacks using a single model. Further, no work has been done to provide an apples-to-apples comparison of published countermeasures in order to assess their generalizability by evaluating them across corpora. In this work, we conduct a review of the literature on spoofing detection using hand-crafted features, deep learning, end-to-end, and universal spoofing countermeasure solutions to detect speech synthesis (SS), voice conversion (VC), and replay attacks. Additionally, we also review integrated solutions to voice spoofing evaluation and speaker verification, adversarial and anti-forensics attacks on voice countermeasures, and ASV. The limitations and challenges of the existing spoofing countermeasures are also presented. We report the performance of these countermeasures on several datasets and evaluate them across corpora. For the experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of the test bed can be found in our GitHub Repository.
翻译:恶意行为者可能试图利用不同的声音威胁攻击来愚弄ASV系统,甚至利用这些系统来散布错误信息。提出了各种对策以发现这些虚假攻击。由于在过去6-7年中在自动扬声器核查系统(ASV)中所做的大量工作,有必要对研究进行分类,并对最新对策进行定性和定量比较。此外,没有一份现有调查文件审查了关于声音威胁的评价和发言者核查、对抗/反敏感攻击反言和ASV本身,或用单一模型来发现多次攻击的统一解决办法。此外,没有开展任何工作来提供对已公布的反言器进行苹果到应用的比较,以便通过对各科室的反言器评估来评估其普遍性。在这项工作中,我们通过手制的语音特征、深层次学习、最终到最后,以及普遍反言道分析方法,以探测语音合成(SS)、声音转换(VC)和反动性攻击(SDRVS),还审查了我们现有反变声器攻击的综合解决办法,还审查了SVSVSDRFA的测试结果。