Weakly Supervised Adaptation to Re-sizing for Image Manipulation Detection on Small Patches

Basic image processing operations like median filtering and Gaussian blurring in general do not change the semantic content of an image, although they are commonly used to cover fingerprints of falsification that does alter image content such as copy-move and splicing. Therefore image forensics researchers are interested in detecting these basic operations. Some existing detectors track local inconsistencies in statistics of the image. However these statistics are very sensitive to image development process. Thus pre-processing operations can be damaging for performances of such detectors. In this paper, we focus on a very common pre-processing operation, i.e., re-sizing, and study how it affects performance when trying to detect several image processing operations on small patches, with Gaussian Mixture Model (GMM) as feature extractor and a Dense Neural Network (DNN) as classifier. We first show performance drops. We then introduce an adaptation method which relies on better fit to testing data for the feature extraction and fine-tuning for the neural network classifier. Experimental results show that our method is able to improve results with very few labeled testing samples. We also present comparisons with an improved version of a recent CNN(Convolutional Neural Network)-based method.