Abstract
Steganography is the technique for secretly hiding messages in media such as text, audio, image, and video without being discovered. Image is one of the most essential media for concealing data, making it hard to identify hidden data not visible to the human eye. In general, the cover image and the encrypted image are symmetrical in terms of dimension size, resolution, and qualities. This makes the difference difficult to perceive with the human eye. As a result, distinguishing between the two symmetric images required the development of methods. Steganalysis is a technique for identifying hidden messages embedded in digital material without having to know the embedding algorithm or the “non-stego” image. Due to their enormous feature vector dimension, which requires more time to calculate, the performance of most existing image steganalysis classification (ISC) techniques is still restricted. Therefore, in this research, we present a steganalysis classification method based on one of the texture features chosen, such as segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). The classifiers employed include Gaussian discriminant analysis (GDA) and naïve Bayes (NB). We used a public database in our proposed method and applied it to IStego100K datasets to be able to assess its performance. The experimental results reveal that in all classifiers, the SFTA feature surpassed all of the texture features, making it a great texture feature for image steganalysis classification. In terms of feature dimension and classification accuracy (CA), a comparison was made between the suggested SFTA-based GDA approach and various current ISC methods. The outcomes of the comparison are obvious show that the proposed method surpasses current methods.
Highlights
Due to the rapid growth of social networking sites, we may see or receive a large number of photographs, but we have no way of knowing whether these images are original or encrypted
Specific steganalysis is created for a particular ality of extracted features, we proposed an image steganalysis classification (ISC) method steganographic embedding as LSB embedding, LSB matching, spread based on three steps:algorithm, First was thesuch pre-processing stage, after which texture features were spectrum, BPCS, JPEG compression, and other transform domains [3], whereas universal steganalysis is a general class steganalysis technique that can be used with any steganographic embedding algorithm, including unknown algorithms [4]
In order to improve the accuracy and reduce the high dimensionality of extracted features, we proposed an image steganalysis classification (ISC) method based on three steps: First was the pre-processing stage, after which texture features were extracted by using segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM)
Summary
Due to the rapid growth of social networking sites, we may see or receive a large number of photographs, but we have no way of knowing whether these images are original or encrypted. We show that the cover image and the encrypted image, in general, are symmetrical in terms of size dimension, resolution, and qualities. The goal of blind steganalysis is to detect steganographic data without knowing the embedding algorithm or the cover image. Steganalysis approaches are classified into two kinds, signature and statistical steganalysis, according to steganalysis detection methods in the literature review.
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