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 …

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 …

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

Face forgery detection is raising ever-increasing interest in computer vision since facial
manipulation technologies cause serious worries. Though recent works have reached …

We propose a new method to detect deepfake images using the cue of the source feature
inconsistency within the forged images. It is based on the hypothesis that images' distinct …

A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are
mostly trained to detect a specific fake method. As a result, these approaches show poor …

With the rapid development of face manipulation technology, it is difficult for human eyes to
distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) …

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 …

Better generative models and larger datasets have led to more realistic fake videos that can
fool the human eye but produce temporal and spatial artifacts that deep learning …

This paper proposes a commonality learning strategy for face video forgery detection to
improve the generalization. Considering various face forgery methods could leave certain …