GAN Image

GAN image detection research focuses on developing robust methods to distinguish between real and artificially generated images, primarily addressing the increasing sophistication of generative models like GANs and diffusion models. Current efforts concentrate on improving the generalization of detectors across various GAN architectures and image manipulations, often employing techniques like frequency analysis, local statistics, and contrastive learning within deep learning frameworks such as convolutional neural networks and visual transformers. The ability to reliably detect GAN-generated images is crucial for combating misinformation, protecting against fraud, and ensuring the integrity of visual information in various applications.