Forensic Analysis of Linear and Nonlinear Image Filtering Using Quantization Noise

Hareesh Ravi, A. Venkata Subramanyam, Sabu Emmanuel

Abstract: The availability of intelligent image editing techniques and antiforensic algorithms, make it convenient to manipulate an image and to hide the artifacts that it might have produced in the process. Real world forgeries are generally followed by the application of enhancement techniques such as filtering and/or conversion of the image format to suppress the forgery artifacts. Though several techniques evolved in the direction of detecting some of these manipulations, additional operations like recompression, nonlinear filtering, and other antiforensic methods during forgery are not deeply investigated. Toward this, we propose a robust method to detect whether a given image has undergone filtering (linear or nonlinear) based enhancement, possibly followed by format conversion after forgery. In the proposed method, JPEG quantization noise is obtained using natural image prior and quantization noise models. Transition probability features extracted from the quantization noise are used for machine learning based detection and classification. We test the effectiveness of the algorithm in classifying the class of the filter applied and the efficacy in detecting filtering in low resolution images. Experiments are performed to compare the performance of the proposed technique with state-of-the-art forensic filtering detection algorithms. It is found that the proposed technique is superior in most of the cases. Also, experiments against popular antiforensic algorithms show the counter antiforensic robustness of the proposed technique.