  Contrast enhancement (CE) forensics has always been ofconcern to image
forensics community. It can provide aneffective tool for recovering image
history and identifyingtampered images. Although several CE forensic
algorithmshave been proposed, their robustness against some processingis still
unsatisfactory, such as JPEG compression and anti-forensic attacks. In order to
attenuate such deficiency, inthis paper we first present a discriminability
analysis of CEforensics in pixel and gray level histogram domains. Then, insuch
two domains, two end-to-end methods based on convo-lutional neural networks
(P-CNN, H-CNN) are proposed toachieve robust CE forensics against pre-JPEG
compressionand anti-forensics attacks. Experimental results show that
theproposed methods achieve much better performance than thestate-of-the-art
schemes for CE detection in the case of noother operation and comparable
performance when pre-JPEGcompression and anti-foresics attacks is used.
