Recently the proliferation of highly realistic synthetic images facilitated through a variety of
GANs and Diffusions has significantly heightened the susceptibility to misuse. While the …

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 …

Deepfake detection faces a critical generalization hurdle with performance deteriorating
when there is a mismatch between the distributions of training and testing data. A broadly …

Face Forgery videos have elicited critical social public concerns and various detectors have
been proposed. However, fully-supervised detectors may lead to easily overfitting to specific …

Deepfake technology has given rise to a spectrum of novel and compelling applications.
Unfortunately, the widespread proliferation of high-fidelity fake videos has led to pervasive …

In today's digital landscape, video and image data have emerged as pivotal and widely
adopted means of communication. They serve not only as a ubiquitous mode of conveying …

Detecting and grounding multi-modal media manipulation (DGM 4) has become
increasingly crucial due to the widespread dissemination of face forgery and text …

Detecting diffusion-generated images has recently developed as an emerging research
area. Existing diffusion-based datasets predominantly focus on general image generation …

The generalization ability of deepfake detectors is vital for their applications in real-world
scenarios. One effective solution to enhance this ability is to train the models with manually …

Large numbers of synthesized videos from diffusion models pose threats to information
security and authenticity, leading to an increasing demand for generated content detection …