Blind Median Filtering Detection Using Auto-Regressive Model and Markov Chain

Abstract

Establishing the processing history of an image is important for robot vision. In this paper, an improved method for median filtering detection is proposed. That is, detect whether an image has been processed by median filtering. First, we analyze the statistical properties of median filtering residual and find that it is suitable for exposing fingerprints of median filtering. Then, the new feature set on median filtering residual is constructed by incorporating transition probability matrices of Markov chain with coefficients of auto-regressive model. A dimensionality reduction method is developed to lower the feature dimensionality. The final feature set is fed into support vector machines to construct a detector. Due to the distinction property of median filtering residual as well as compensated effect between transition probability and auto-regressive model, experimental results on large image database demonstrate that the proposed method is effectively in median filtering detection, even for images with heavy JPEG compression or at a low resolution. The performance of proposed detector outperforms prior arts. Additionally, the proposed method demonstrates good generalization ability.