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Recently, steganalysis based on hypothesis test theory becomes a focus. However, the correlation between adjacent pixels is not exploited and this is obviously not reasonable. In this paper, a new detector for least significant bit matching (LSBM) steganography is proposed with the consideration of pixel correlation. The cover pixels are modeled by multivariate Gaussian distribution and a new detector...
Least significant bit (LSB) matching is a well-known steganographic method, which can embed large payload into cover data with good visual and statistical imperceptibility. However, it disturbs the correlation of adjacent pixels in smooth image regions as it randomly modifies half of the payload pixels by 1. Local binary patterns (LBPs) are first proposed as texture features, and can summarize local...
Currently, images could be created and tampered easily with low cost image devices and sophisticated editing software. One cannot trust any image to be integrated anymore. Thus there is a strong need to investigate image tampering identification. This paper focuses on blind scheme of tampering identification which is practical in most scenarios. Given an image, our idea is to find its reference image...
In this paper, we present an improved method for detecting LSB matching steganography in gray-scale image. Our improvements focus on three aspects: (1) instead of using the amplitude of local extrema of the image's histogram in the previous work, we turn to considering the sum of the amplitude of each point in the histogram; (2) incorporating the calibration (downsample) technique with the current...
In this paper, based on a careful investigation on the calibration (downsample) technique, we improve two detectors for detecting LSB matching: calibrated HCF COM and calibrated adjacency HCF COM. Instead of using the COM (center of mass) of the HCF (histogram characteristic function), we consider the ratio of the histogram's DFT coefficients of the image to the corresponding coefficients of the down-sampled...
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