Neural networks have proven to be a highly effective tool for solving complex problems in
many areas of life. Recently, their importance and practical usability have further been …

Deepfakes present an emerging threat in cyberspace. Recent developments in machine
learning make deepfakes highly believable, and very difficult to differentiate between what is …

This work presents our systems for the ADD2022 challenge. The ADD2022 challenge is the
first audio deep synthesis detection challenge, which aims to spot various kinds of fake …

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 …

The ability to distinguish between authentic and fake audio is become increasingly difficult
due to the increasing accuracy of text-to-speech models, posing a serious threat to speaker …

Voice phishing (vishing) is increasingly popular due to the development of speech synthesis
technology. In particular, the use of deep learning to generate an arbitrary-content audio clip …

The emergence of audio deepfakes has prompted concerns over reputational integrity and
dependability. Deepfakes with audio can now be produced more easily, which makes it …

Audio-visual deepfake detection is the process of identifying and detecting deepfakes that
have been generated using both audio and visual content with AI algorithms. Most existing …

In this paper, we describe our submitted systems to the ADD2023 Challenge Track 3–
Deepfake algorithm recognition (AR). This task requires not only identifying known deepfake …

The current voice biometric systems have no natural mechanics to defend against deepfake
spoofing attacks. Thus, supporting these systems with a deepfake detection solution is …