Over the last few decades, rapid progress in AI, machine learning, and deep learning has
resulted in new techniques and various tools for manipulating multimedia. Though the …

The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

With generative models proliferating at a rapid rate, there is a growing need for general
purpose fake image detectors. In this work, we first show that the existing paradigm, which …

Differences in forgery attributes of images generated in CNN-synthesized and image-editing
domains are large, and such differences make a unified image forgery detection and …

In this paper, we consider the face swap** detection from the perspective of face identity.
Face swap** aims to replace the target face with the source face and generate the fake …

In this paper, we present novel synthetic training data called self-blended images (SBIs) to
detect deepfakes. SBIs are generated by blending pseudo source and target images from …

Deepfake detection remains a challenging task due to the difficulty of generalizing to new
types of forgeries. This problem primarily stems from the overfitting of existing detection …

Existing face forgery detectors mainly focus on specific forgery patterns like noise
characteristics, local textures, or frequency statistics for forgery detection. This causes …

Recently, there has been a significant advancement in image generation technology, known
as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse …

In this paper, we analyse the generalization ability of binary classifiers for the task of
deepfake detection. We find that the stumbling block to their generalization is caused by the …