  Contrast enhancement (CE) forensics techniques have always been of great
interest for image forensics community, as they can be an effective tool for
recovering image history and identifying tampered images. Although several CE
forensic algorithms have been proposed, their accuracy and robustness against
some kinds of processing are still unsatisfactory. In order to attenuate such
deficiency, in this paper we propose a new framework based on dual-domain
fusion convolutional neural network to fuse the features of pixel and histogram
domains for CE forensics. Specifically, we first present a pixel-domain
convolutional neural network (P-CNN) to automatically capture the patterns of
contrast-enhanced images in the pixel domain. Then, we present a
histogram-domain convolutional neural network (H-CNN) to extract the features
in the histogram domain. The feature representations of pixel and histogram
domains are fused and fed into two fully connected layers for the
classification of contrast-enhanced images. Experimental results show that the
proposed method achieve better performance and is robust against pre-JPEG
compression and anti-forensics attacks. In addition, a strategy for performance
improvement of CNN-based forensics is explored, which could provide guidance
for the design of CNN-based forensics tools.
