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Abstract. In this paper, a new steganalysis scheme is presented to effectively detect the advanced JPEG steganography. For this purpose, we first choose to work on JPEG 2-D arrays formed from the magnitudes of JPEG quantized block DCT coefficients. Difference JPEG 2-D arrays along horizontal, vertical and diagonal directions are then used to enhance changes caused by JPEG steganography. Markov process is applied to modeling these difference JPEG 2-D arrays so as to utilize the second order statistics for steganalysis. In addition to the utilization of difference JPEG 2-D arrays, a thresholding technique is developed to greatly reduce the dimensionality of transition probability matrices, i.e., the dimensionality of feature vectors, thus making the computational complexity of the proposed scheme manageable. The experimental works are presented to demonstrate that the proposed scheme has outperformed the existing steganalyzers in attacking OutGuess, F5, and MB1.

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In this paper, a novel statistical model based on Benford's law for the probability distributions of the first digits of the block-DCT and quantized JPEG coefficients is presented. A parametric logarithmic law, i.e., the generalized Benford's law, is formulated. Furthermore, some potential applications of this model in image forensics are discussed in this paper, which include the detection of JPEG compression for images in bitmap format, the estimation of JPEG compression Qfactor for JPEG compressed bitmap image, and the detection of double compressed JPEG image. The results of our extensive experiments demonstrate the effectiveness of the proposed statistical model.

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Abstract

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, we propose a blind, passive, yet effective splicing detection approach based on a natural image model. This natural image model consists of statistical features extracted from the given test image as well as 2-D arrays generated by applying to the test images multi-size block discrete cosine transform (MBDCT). The statistical features include moments of characteristic functions of wavelet subbands and Markov transition probabilities of difference 2-D arrays. To evaluate the performance of our proposed model, we further present a concrete implementation of this model that has been designed for and applied to the Columbia Image Splicing Detection Evaluation Dataset. Our experimental works have demonstrated that this new splicing detection scheme outperforms the state of the art by a significant margin when applied to the above-mentioned dataset, indicating that the proposed approach possesses promising capability in splicing detection.

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