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 …

Advancements in deep learning techniques and the availability of free, large databases
have made it possible, even for non-technical people, to either manipulate or generate …

Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without
design skills, to create realistic images from simple text inputs. With powerful personalization …

In recent years, as various realistic face forgery techniques known as DeepFake improves
by leaps and bounds, more and more DeepFake detection techniques have been proposed …

DeepFake detection aims to differentiate falsified faces from real ones. Most approaches
formulate it as a binary classification problem by solely mining the local artifacts and …

Unpaired image-to-image translation has broad applications in art, design, and scientific
simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings …

Abstract In recent decades, Generative Adversarial Network (GAN) and its variants have
achieved unprecedented success in image synthesis. However, well-trained GANs are …

Deep learning is used to address a wide range of challenging issues including large data
analysis, image processing, object detection, and autonomous control. In the same way …

Despite the development of effective deepfake detectors in recent years, recent studies have
demonstrated that biases in the data used to train these detectors can lead to disparities in …

In this paper, we focus on identifying the limitations and shortcomings of existing deepfake
detection frameworks. We identified some key problems surrounding deepfake detection …