Improving the Generalization of Colorized Image Detection with Enhanced Training of CNN

Image colorization achieves more and more realistic results with the increasing power of recent deep learning techniques. It becomes more difficult to identify the synthetic colorized images by human eyes. In the literature, handcrafted-feature-based and convolutional neural network (CNN)-based forensic methods are proposed to distinguish between natural images (NIs) and colorized images (CIs). Although a recent CNN-based method achieves very good detection performance, an important issue (i.e., the blind detection problem) still remains and is not thoroughly studied. In this work, we focus on this challenging scenario of blind detection, i.e., no training sample is available from “unknown” colorization algorithm that we may encounter during the testing phase. This blind detection performance can be regarded as the generalization capability of a forensic detector. In this paper, we propose to first automatically construct negative samples through linear interpolation of paired natural and colorized images. Then, we progressively insert these negative samples into the original training dataset and continue to train the network. Experimental results demonstrate that our enhanced training can significantly improve the generalization performance of different CNN models.