  Detection of contrast adjustments in the presence of JPEG postprocessing is
known to be a challenging task. JPEG post processing is often applied
innocently, as JPEG is the most common image format, or it may correspond to a
laundering attack, when it is purposely applied to erase the traces of
manipulation. In this paper, we propose a CNN-based detector for generic
contrast adjustment, which is robust to JPEG compression. The proposed system
relies on a patch-based Convolutional Neural Network (CNN), trained to
distinguish pristine images from contrast adjusted images, for some selected
adjustment operators of different nature. Robustness to JPEG compression is
achieved by training the CNN with JPEG examples, compressed over a range of
Quality Factors (QFs). Experimental results show that the detector works very
well and scales well with respect to the adjustment type, yielding very good
performance under a large variety of unseen tonal adjustments.
