GRAFT: Unsupervised Adaptation to Resizing for Detection of Image Manipulation

Abstract

A large number of methods for forensics of image manipulation relies on detecting fingerprints in residuals or noises. Therefore, these detection methods are bound to be sensitive to noise generated by the image acquisition process, as well as any pre-processing. We show that a difference in pre-processing pipelines between training and testing sets induces performance losses for various classifiers. We focus on a particular pre-processing: resizing. It corresponds to a typical scenario where images may be resized (e.g., downscaled to reduce storage) prior to being manipulated. This performance loss due to pre-resizing could be troublesome but has been rarely investigated in the image forensics field. We propose a new and effective adaptation method for one state-of-the-art image manipulation detection pipeline, and we call our proposed method Gaussian mixture model Resizing Adaptation by Fine-Tuning (GRAFT). Adaptation is performed in an unsupervised fashion, i.e., without using any ground-truth label in the pre-resized testing domain, for the detection of image manipulation on very small patches. Experimental results show that the proposed GRAFT method can effectively improve the detection accuracy in this challenging scenario of unsupervised adaptation to resizing pre-processing.