Abstract
In image steganography, the most popular and widely used techniques is the least significant bit (LSB) that hide data into a cover-image in a spatial and discrete cosine transform (DCT) domain as well. Beside the LSB technique, there is other technique that is also influential i.e support vector machine (SVM) normally used to strengthen the embedding algorithm. Whatever techniques used in the image steganography field, the main purpose is to keep the existence of the secret-message secret. This paper designing the new model is proposed called StegaSVM-Shifted LSB model in DCT domain to preserve the imperceptibility and increase the robustness of stego-images. The StegaSVM-Shifted LSB model that has been proposed that utilize HVS and embedding technique through Shifted LSB showed a good performance.
Highlights
The importance of information security has increased due to the increased use of computers and the Internet
Whatever techniques used in the image steganography field,the main purpose is to keep the existence of the secret-message secret
The StegaSVM-Shifted least significant bit (LSB) model that has been proposed that utilize human visual system (HVS) and embedding technique through Shifted LSB showed a good performance
Summary
In StegaSVM classification, the classified cover-image will be utilized and the non-smooth area, the most appropriate and imperceptible is preferred to be used during the embedding process. All works concentrate on imperceptibility to utilize the SVMs good ability in learning the relationship between hidden-information and cover-image that is useful to be used in embedding and extracting functions [16][18]. His attempt is fruitful in utilizing SVM to classify the cover-image into smooth and non-smooth areas by using 1024 bits as training bits For security purposes, he employs a pseudo-random number generator (PRNG) to randomly pick the embedding position before the secret-bits is embedded by modifying the blue channel of color components. 3.3 StegaSVM-Shifted LSB Embedding Model The StegaSVM-Shifted LSB embedding model process is shown in Figure 7 in this paper
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