  In recent years, image forensics has attracted more and more attention, and
many forensic methods have been proposed for identifying image processing
operations. Up to now, most existing methods are based on hand crafted
features, and just one specific operation is considered in their methods. In
many forensic scenarios, however, multiple classification for various image
processing operations is more practical. Besides, it is difficult to obtain
effective features by hand for some image processing operations. In this paper,
therefore, we propose a new convolutional neural network (CNN) based method to
adaptively learn discriminative features for identifying typical image
processing operations. We carefully design the high pass filter bank to get the
image residuals of the input image, the channel expansion layer to mix up the
resulting residuals, the pooling layers, and the activation functions employed
in our method. The extensive results show that the proposed method can
outperform the currently best method based on hand crafted features and three
related methods based on CNN for image steganalysis and/or forensics, achieving
the state-of-the-art results. Furthermore, we provide more supplementary
results to show the rationality and robustness of the proposed model.
