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With the advent of automated speaker verifcation (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofng attacks to fool those same systems. Various counter measures have been proposed to detect these spoofing attacks, but current oferings in this arena fall short of a unifed and generalized approach applicable in real-world scenarios. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classifed, and qualitative and quantitative comparisons of state-of-the-art (SOTA) counter measures should be performed to assess the efectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoofng detection using hand-crafted features, deep learning, and end-to-end spoofng countermeasure solutions to detect logical access attacks, such as speech synthesis and voice conversion, and physical access attacks, i.e., replay attacks. Additionally, we review integrated and unifed solutions to voice spoofng evaluation and speaker verifcation, and adversarial and anti-forensic attacks on both voice counter measures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofng counter measures are presented, the performance of these counter measures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the generalizability of existing solutions. For the experiments, we employ the ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifers. For reproducibility of the results, the code of the testbed can be found at our GitHub Repository (https://github.com/smileslab/Comparative-Analysis-Voice-Spoofing).
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AEXANet: An End-to-End Deep Learning based Voice Anti-spoofing System
Exponential growth in the use of smart speakers (SS) for the automation of homes, offices, and vehicles has brought a revolution of convenience to our lives. However, these SSs are susceptible to a variety of spoofing attacks, known/seen and unknown/unseen, created using cutting-edge AI generative algorithms. The realistic nature of these powerful attacks is capable of deceiving the automatic speaker verification (ASV) engines of these SSs, resulting in a huge potential for fraud using these devices. This vulnerability highlights the need for the development of effective countermeasures capable of the reliable detection of known and unknown spoofing attacks. This paper presents a novel end-to-end deep learning model, AEXANet, to effectively detect multiple types of physical- and logical-access attacks, both known and unknown. The proposed countermeasure has the ability to learn low-level cues by analyzing raw audio, utilizes a dense convolutional network for the propagation of diversified raw waveform features, and strengthens feature propagation. This system employs a maximum feature map activation function, which improves the performance against unseen spoofing attacks while making the model more efficient, enabling the model to be used for real-time applications. An extensive evaluation of our model was performed on the ASVspoof 2019 PA and LA datasets, along with TTS and VC samples, separately containing both seen and unseen attacks. Moreover, cross corpora evaluation using the ASVspoof 2019 and ASVspoof 2015 datasets was also performed. Experimental results show the reliability of our method for voice spoofing detection.
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- Award ID(s):
- 1815724
- PAR ID:
- 10356298
- Date Published:
- Journal Name:
- Workshop on Artificial Intelligence for Multimedia Forensics and Disinformation Detection (AI4MFDD)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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