Determining which processing operations were used to edit an image and the order in which they were applied is an important task in image forensics. Existing approaches to detecting single manipulations have proven effective, however, their performance may significantly deteriorate if the processing occurs in a chain of editing operations. Thus, it is very challenging to detect the processing used in an ordered chain of operations using traditional forensic approaches. First attempts to perform order of operations detection were exclusively limited to a certain number of editing operations where feature extraction and order detection are disjoint. In this paper, we propose a new data-driven approach to jointly extract editing detection features, detect multiple editing operations, and determine the order in which they were applied. We design a constrained CNN-based classifier that is able to jointly extract low-level conditional fingerprint features related to a sequence of operations as well as identify an operation's order. Through a set of experiments, we evaluated the performance of our CNN-based approach with different types of residual features commonly used in forensics. Experimental results show that our method outperforms the existing approaches.
Belhassen Bayar, Matthew C. Stamm, "Towards Order of Processing Operations Detection in JPEG-compressed Images with Convolutional Neural Networks" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics, 2018, pp 211-1 - 211-9, https://doi.org/10.2352/ISSN.2470-1173.2018.07.MWSF-211