State-of-the-art deepfake detectors perform well in identifying forgeries when they are
evaluated on a test set similar to the training set, but struggle to maintain good performance …

Recent advances in face forgery techniques produce nearly visually untraceable deepfake
videos, which could be leveraged with malicious intentions. As a result, researchers have …

With advancements of deep learning techniques, it is now possible to generate super-
realistic images and videos, ie, deepfakes. These deepfakes could reach mass audience …

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

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 …

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 …

Public concerns about deepfake face forgery are continually rising in recent years. Most
deepfake detection approaches attempt to learn discriminative features between real and …

DeepFakes detection approaches have to be agnostic across generation type, quality, and
appearance to provide a generalizable DeepFakes detector. Limited generalizability will …

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 …

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 …