Detection of Fake Images Via The Ensemble of Deep Representations from Multi Color Spaces

Recently, the success of generating fake images by Generative Adversarial Network (GAN) has threatened the authentication of digital images. To address this issue, several automated fake image detectors have been proposed. However, current methods remain vulnerable when testing samples undergo post-processing attacks. In this work, we employed residual signals of chrominance components from multi color spaces, including YCbCr, HSV and Lab, to learn robust deep representations via the well-designed shallow convolutional neural network (CNN). Then, the learned deep representations from different color spaces are concatenated and then fed into the Random Forest (RF), which is the widely used ensemble classifier, to obtain final detection results. Extensive experiments are conducted on the fake image dataset generated by the advanced GAN technique. Experimental results demonstrate the proposed scheme outperforms state-of-the-art methods and achieves the promising average detection accuracy (above 99%) under several post-processing attacks, such as Gaussian blurring and so on.