  Image colorization achieves more and more realistic results with the
increasing computation power of recent deep learning techniques. It becomes
more difficult to identify the fake colorized images by human eyes. In this
work, we propose a novel forensic method to distinguish between natural images
(NIs) and colorized images (CIs) based on convolutional neural network (CNN).
Our method is able to achieve high classification accuracy and cope with the
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 a generalization
performance. First, we design and implement a base network, which can attain
better performance in terms of classification accuracy and generalization (in
most cases) compared with state-of-the-art methods. Furthermore, we design a
new branch, which analyzes smaller regions of extracted features, and insert it
into the above base network. Consequently, our network can not only improve the
classification accuracy, but also enhance the generalization in the vast
majority of cases. To further improve the performance of blind detection, we
propose to 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 method can
achieve stable and high generalization performance when tested against
different state-of-the-art colorization algorithms.
