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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)
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(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)
In past several years so many feature sets for steganalysis were proposed to detect stego images. These features based on different ideas and were considered to be effective for most steganography schemes. However a systematically comparison of these features have not been made in previous papers. In order to get a view of performance of current features in(More)
In this paper, we propose to use Multi-Scale Convolutional Neural Networks (CNNs) to conduct forgery localization in digital image forensics. A unified CNN architecture for input sliding windows of different scales is designed. Then, we elaborately design the training procedures of CNNs on sampled training patches in the IEEE IFS-TC Image Forensics(More)
The GFR (Gabor Filter Residual) features, built as histograms of quantized residuals obtained with 2D Gabor filters, can achieve competitive detection performance against adaptive JPEG steganography. In this paper, an improved version of the GFR is proposed. First, a novel histogram merging method is proposed according to the symmetries between different(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)
In this paper, we present a universal embedding strategy for batch adaptive steganography in both spatial and JPEG domain. This strategy can make up for the problem when applying batch steganography to adaptive steganography in JPEG domain meanwhile improve the secure performance in spatial domain. When embedding, the strategy are employed to determine the(More)