The Forensicability of Operation Detection in Image Operation Chain

A large number of forensics research focus on operation detection to reveal the evidence of forgery action in the digital image. In the early works, analyst firstly model the probability distribution of single operation, and design the forensic tools based on feature extraction and machine learning based classifier. With increasing dimension of the feature and facing multiple operations detection scenario, the physical meaning of the feature gradually become ambiguous. Especially, since deep learning algorithm was used in forensic research, the automatic feature selection and making decision with high performance of classification conceals the intrinsic forensic clues. In this paper, we explore the availability of feature for operation detection in the operation chain, so called forensicability. An anti-forensic attack algorithm is introduced to formulate the impact on the feature due to the following operation. We propose two measurements: attack angle and scale, mutual information scale to indicate the forensic feature variation after the image manipulated by the following operation. The uncoupled relationship can be revealed by our methods. In the experiments, four operation chains involving ten operations are considered as the case study. The results are encouraging and improve the explanation of the forensics method based on high dimensional features.