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, handcraftedfeature-
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.