While diffusion models can learn complex distributions, sampling requires a computationally expensive iterative process.

While these methods have shown promise, they overlook the simulation of frequency distribution in pseudo-fake faces, limiting the learning of generic forgery traces in-depth. To address this, this paper introduces FreqBlender, a new method that can generate pseudo-fake faces by blending frequency knowledge.

Large transformer-based foundation models have been commonly used as pre-trained models that can be adapted to different challenging datasets and settings with state-of-the-art generalization performance.

This new task holds significant potential for human-centric applications such as assistive robotics and sports analysis.

We investigate here the extent to which large language models pretrained on source code can solve PBE.

Deep Neural Networks (DNNs) have revolutionized artificial intelligence, achieving impressive results on diverse data types, including images, videos, and texts.