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 …

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 …

This paper presents a new approach for the detection of fake videos based on the analysis
of style latent vectors and their abnormal behavior in temporal changes in the generated …

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 …

With the rapid growth in deepfake video content we require improved and generalizable
methods to detect them. Most existing detection methods either use uni-modal cues or rely …

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are
extensively being harnessed across a diverse range of domains, eg, forensic science …

Detecting and recognizing deepfakes is a pressing issue in the digital age. In this study, we
first collected a dataset of pristine images and fake ones properly generated by nine different …

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 …

Face forgery detection is essential in combating malicious digital face attacks. Previous
methods mainly rely on prior expert knowledge to capture specific forgery clues, such as …

The DeepFake, which generates synthetic content, has sparked a revolution in the fight
against deception and forgery. However, most existing DeepFake detection methods mainly …