Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

With the advent of automated speaker verification (ASV) systems comes an equal and
opposite development: malicious actors may seek to use voice spoofing attacks to fool those …

In the rapidly evolving digital landscape, the generation of fake visual, audio, and textual
content poses a significant threat to the trust of society, political stability, and integrity of …

Transferable adversarial attacks against Deep neural networks (DNNs) have received broad
attention in recent years. An adversarial example can be crafted by a surrogate model and …

This paper presents a review of techniques involved in the creation and detection of audio
deepfakes, the first section provides information about general deep fakes. In the second …

Voice spoofing detection is a technique for enhancing the security of automatic speaker
verification system, but the existing research still faces problems such as weak detection …

Most of the voice spoofing detection methods are designed for specific kinds of spoofing
attacks, synthetic or replay. In practice, however, there is no prior information about these …

Abstract Machine Learning, and in general Artificial Intelligence approaches, brought a great
advance in each and every field of Computer Science increasing accuracy levels of …

Abstract Automatic Speaker Verification (ASV) systems are vulnerable to a variety of voice
spoofing attacks, eg, replays, speech synthesis, etc. The imposters/fraudsters often use …

Voice anti-spoofing is an important step for secure speaker verification in voice-enabled
Internet of Things (IoT) systems. Most voice spoofing detection methods require significant …