The availability of smart devices leads to an exponential increase in multimedia content.
However, the rapid advancements in deep learning have given rise to sophisticated …

Audio deepfake detection (ADD) is crucial to combat the misuse of speech synthesized from
generative AI models. Existing ADD models suffer from generalization issues, with a large …

The detection of spoofing speech generated by unseen algorithms remains an unresolved
challenge. One reason for the lack of generalization ability is that traditional detecting …

As speech synthesis systems continue to make remarkable advances in recent years, the
importance of robust deepfake detection systems that perform well in unseen systems has …

Current trends in audio anti-spoofing detection research strive to improve models' ability to
generalize across unseen attacks by learning to identify a variety of spoofing artifacts. This …

ASVspoof5, the fifth edition of the ASVspoof series, is one of the largest global audio security
challenges. It aims to advance the development of countermeasure (CM) to discriminate …

A major difficulty in speech spoofing detection lies in improving the generalization ability to
detect unknown forgery methods. However, most previous methods do not consider the …

In recent years, most state-of-the-art approaches for spoofed speech detection have been
based on convolutional neural networks (CNNs). Most neural networks, including CNNs, are …

Voice is one of the most widely used media for information transmission in human society.
While high-quality synthetic voices are extensively utilized in various applications, they pose …

Partially manipulating a sentence can greatly change its meaning. Recent work shows that
countermeasures (CMs) trained on partially spoofed audio can effectively detect such …