Detection of contrast adjustments in the presence of JPEG post processing 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 a JPEG-aware version of the CNN, i.e., feeding the CNN with JPEG examples, compressed over a range of Quality Factors (QFs). Experimental results show that the detector works very well under a wide range of QFs and scales well with respect to the adjustment type, yielding very good performance under a large variety of unseen tonal adjustments.