Abstract Generative Adversarial Networks (GAN) have led to the generation of very realistic
face images, which have been used in fake social media accounts and other disinformation …

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are
extensively being harnessed across a diverse range of domains, eg, forensic science …

RETRACTED ARTICLE: Evaluating the effectiveness of rationale-augmented convolutional
neural networks for deepfake detection: Evaluating the effectiveness of rationale-augmented …

In recent times, the widespread dissemination of misinformation through realistic artificially
generated images and deep faked videos has become a common issue, particularly …

Using deep-learning technologies, both text-to-speech (TTS) and voice conversion (VC)
methods can generate fake speech effectively, making it challenging to differentiate between …

The potential to weaponize deepfakes is growing at an alarming rate. The study aimed to
explore how education can help youth develop resilience to malicious deepfakes and the …

Deepfakes are a form of synthetic media that uses deep-learning technology to create fake
images, video, and audio. The emergence of this technology has inspired much commentary …

The rise of large language models (LLMs) generating human-like text has raised concerns
about misuse, especially in low-resource languages like Urdu. To address this gap, we …

The aim of this article is to look at algorithmic media and how they shift and alter everyday
media-related practices and content, and with it power relations as well as agency between …

Face forgery detection aims to identify manipulated or altered facial images or videos
created using artificial intelligence. Existing detection methods exhibit favorable …