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45 dimensional feature vectors were extracted from DCT and spatial domains of images to defeat steganographic schemes in this paper. Compared with commonly used steganalytic algorithms, the presented method offers much better performances in double JPEG compressed covers and their stegos. However, when our method was applied to detect whether multiple sources of images containing hidden data or not,...
This paper presents a new scheme for steganalysis of random LSB embedding, capable of applying to any kind of digital signal in both spatial and transform domains. The proposed scheme is based on defining a space whose elements relate to higher-order statistical properties of the signal and looking for special subsets, which we call closure of sets (CoS) in this space. We use this scheme for steganalysis...
In this paper we present a novel LSB matching steganalysis method based on feature vectors derived from co-occurrence matrix in spatial domain, which is sensitive to data embedding process. This matrix is derived from an image that some of its most significant bit planes are removed. By this preprocessing in addition to decrease the size of feature vector also preserve effects of embedding. We investigate...
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