Deepfakes are generated using sophisticated deep-learning models to create fake images
or videos. As the techniques for creating deepfakes improve, issues like defamation …

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake
videos that can be difficult to distinguish from real ones. The rise of social media platforms …

Deepfake image detection has emerged as an important area of research due to its wide-
ranging implications for various security systems. In particular, in the field of deep learning …

Deepfake is utilized in synthetic media to generate fake visual and audio content based on a
person's existing media. The deepfake replaces a person's face and voice with fake media …

Different CNNs models do not perform well in deepfake detection in cross datasets. This
paper proposes a deepfake detection model called DeepfakeNet, which consists of 20 …

Deep learning is an effective and useful technique that has been widely applied in a variety
of fields, including computer vision, machine vision, and natural language processing …

Deepfakes generation approaches have made it possible even for less technical users to
generate fake videos using only the source and target images. Thus, the threats associated …

Deep learning has been successfully applied to solve various complex problems ranging
from big data analytics to computer vision and human-level control. Deep learning advances …

In the recent years, social media has grown to become a major source of information for
many online users. This has given rise to the spread of misinformation through deepfakes …

In this work, we created a new large-scale unconstrained high-quality Deepfake Image
(DFIM-HQ) dataset containing 140K images. Compared to existing datasets, this dataset …