A maximum relevancy and minimum redundancy feature selection approach for median filtering forensics

The forensics of the median filtering is a challenging task due to its content preserving nature. Several methods have been proposed for median filtering forensics in digital images. However the performance of these methods deteriorates for compressed images, small resolutions of images and for anti-forensic operations. Moreover large feature set dimensions of these methods also pose a computational challenge. This paper proposes, a 8-dimensional feature set, derived from two state-of-the-art techniques by employing maximum relevancy and minimum redundancy (mRMR) feature selection approach. Features are selected by mRMR on the basis of distance correlation as an association measure. Extensive experiments are performed to evaluate the efficacy of proposed method through six different databases. The proposed method outperforms state-of-the-art techniques for uncompressed images, compressed images at low quality factors, low resolutions images and for an anti-forensic operation. The performance of the proposed method is also compared with convolutional neural network (CNN) based features for the detection of median filtering at low resolutions and for compressed images. Also, experimental results support the performance of proposed method over other manipulations (average and Gaussian filtering).