Although effective deepfake detection models have been developed in recent years recent
studies have revealed that these models can result in unfair performance disparities among …

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 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 …

Facial forgery by deepfakes has raised severe societal concerns. Several solutions have
been proposed by the vision community to effectively combat the misinformation on the …

Recently, Deepfake has drawn considerable public attention due to security and privacy
concerns in social media digital forensics. As the wildly spreading Deepfake videos on the …

A fast-paced development of DeepFake generation techniques challenge the detection
schemes designed for known type DeepFakes. A reliable Deepfake detection approach …

Deepfake detection is gaining significant importance in the research community. While most
of the research efforts are focused around high-quality images and videos, deepfake …

A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of
a standardized, unified, comprehensive benchmark. This issue leads to unfair performance …

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 …

Deepfake has recently raised a plethora of societal concerns over its possible security
threats and dissemination of fake information. Much research on deepfake detection has …