HSB-SPAM: An Efficient Image Filtering Detection Technique

Median filtering is being used extensively for image enhancement and anti-forensics. It is also being used to disguise the traces of image processing operations such as JPEG compression and image resampling when utilized in image de-noising and smoothing tool. In this paper, a robust image forensic technique namely HSB-SPAM is proposed to assist in median filtering detection. The proposed technique considers the higher significant bit-plane (HSB) of the image to highlight the statistical changes efficiently. Further, multiple difference arrays along with the first order pixel difference is used to separate the pixel difference, and Laplacian pixel difference is applied to extract a robust feature set. To compact the size of feature vectors, the operation of thresholding on the difference arrays is also utilized. As a result, the proposed detector is able to detect median, mean and Gaussian filtering operations with higher accuracy than the existing detectors. In the experimental results, the performance of the proposed detector is validated on the small size and post JPEG compressed images, where it is shown that the proposed method outperforms the state of art detectors in the most of the cases.

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

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.