CNN-Based Detection of Generic Constrast Adjustment with JPEG Post-processing
This addresses the problem of detecting image manipulations for forensic analysis, particularly in cases where JPEG compression is involved, but it is incremental as it builds on existing CNN methods for specific adjustments.
The paper tackled the challenging detection of contrast adjustments in images that have undergone JPEG compression, proposing a CNN-based detector that is robust to JPEG post-processing and achieves very good performance across a variety of unseen tonal adjustments.
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.