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 …

Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

Differences in forgery attributes of images generated in CNN-synthesized and image-editing
domains are large, and such differences make a unified image forgery detection and …

In this paper, we present novel synthetic training data called self-blended images (SBIs) to
detect deepfakes. SBIs are generated by blending pseudo source and target images from …

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

Existing face forgery detection models try to discriminate fake images by detecting only
spatial artifacts (eg, generative artifacts, blending) or mainly temporal artifacts (eg, flickering …

With the springing up of face synthesis techniques, it is prominent in need to develop
powerful face forgery detection methods due to security concerns. Some existing methods …

Recent studies in deepfake detection have yielded promising results when the training and
testing face forgeries are from the same dataset. However, the problem remains challenging …

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 …

Current face forgery detection methods achieve high accuracy under the within-database
scenario where training and testing forgeries are synthesized by the same algorithm …