Identifying deep network generated images using disparities in color components

Researchers at Shenzhen University have recently devised a method to detect images generated by deep neural networks. Their study, pre-published on arXiv, identified a set of features to capture color image statistics that can detect images generated using current artificial intelligence tools. "Our research was inspired by the rapid development of image generative models and the spread of generated fake images," Bin Li, one of the researchers who carried out the study, told Tech Xplore. "With the rise of advanced image generative models, such as generative adversarial networks (GAN) and variational autoencoders, images generated by deep networks become more and more photorealistic, and it is no longer easy to identify them with human eyes, which entails serious security risks." Recently, several researchers and global media platforms have expressed their concern with the risks posed by artificial neural networks trained to generate images. For instance, deep learning algorithms such as generative adversarial networks (GAN) and variational autoencoders could be used to generate realistic images and videos for fake news or could facilitate online frauds and the counterfeit of personal information on social media.