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 rapid progress in recent years, techniques that generate and manipulate
multimedia content can now provide a very advanced level of realism. The boundary …

State-of-the-art deep neural networks are still struggling to address the catastrophic
forgetting problem in continual learning. In this paper, we propose one simple paradigm …

Every major technical invention resurfaces the dual-use dilemma—the new technology has
the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such …

In this paper we propose a novel image representation called face X-ray for detecting
forgery in face images. The face X-ray of an input face image is a greyscale image that …

The free access to large-scale public databases, together with the fast progress of deep
learning techniques, in particular Generative Adversarial Networks, have led to the …

Deep learning has been successfully applied to solve various complex problems ranging
from big data analytics to computer vision and human-level control. Deep learning advances …

Abstract Generative Adversarial Networks (GANs) can generate realistic fake face images
that can easily fool human beings. On the contrary, a common Convolutional Neural …

The aim of this work is to explore the potential of pre-trained vision-language models (VLMs)
for universal detection of AI-generated images. We develop a lightweight detection strategy …

The advent of deep learning has brought a significant improvement in the quality of
generated media. However, with the increased level of photorealism, synthetic media are …