Qingxiao Guan

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This paper focuses on image steganalysis. We use higher order image statistics based on neighborhood information of pixels (NIP) to detect the stego images from original ones. We use subtracting gray values of adjacent pixels to capture neighborhood information, and also make use of ―rotation invariant‖ property to reduce the dimensionality for the whole(More)
Blind quantitative steganalysis is about revealing more details about hidden information without any prior knowledge of steganograghy. Machine learning can be used to estimate some properties of hidden message for blind quantitative steganalysis. We propose a quantitative steganalysis method based on fusion of different steganalysis features and the(More)
In this paper, we present a new kind of near-optimal double-layered syndrome-trellis codes (STCs) for spatial domain steganography. The STCs can hide longer message or improve the security with the same-length message comparing to the previous double-layered STCs. In our scheme, according to the theoretical deduction we can more precisely divide the secret(More)
Feature is a key part for steganalysis. In this paper we propose a spatial feature set for image steganalysis, named Local Information Feature (LIF), to increase the diversity of spatial steganalysis feature and improve its performance. It also provide a heuristic framework for designing steganalysis feature through 3 steps. It first collects local(More)
With the development of steganography, it is required to build high-dimensional feature spaces to detect those sophisticated steganographic schemes. However, the huge time cost prevents the practical deployment of high-dimensional features for steganalysis. SRM and DCTR are important steganalysis feature sets in spatial domain and JPEG domain, respectively.(More)
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