Abstract
In this paper, a new steganalysis method is introduced based on human visual system. Steganalysis uses the effect of steganography on the statistical characteristics to detect if such effect exists or not. Steganography methods do not have the same effect on all of the pixels of an image. We use local information to select the best area. We cannot use each individual pixel for feature extraction, so we use blocks. At first, segmentation and clustering algorithm are employed to detect the best segments for steganalysis. In the next step, the features based on wavelet are extracted. At the end, Support Vector Machine is applied as the classifier. The performance of this algorithm is verified by experimental results. The results show that the detection accuracy of our method reaches 98.67% for true positive and 90.67% for true negative when 100% capacity of the image is used with spread spectrum steganography algorithm.
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Bakhshandeh, S., Jamjah, J.R., Azami, B.Z. (2009). Blind Image Steganalysis Based on Local Information and Human Visual System. In: Ślęzak, D., Pal, S.K., Kang, BH., Gu, J., Kuroda, H., Kim, Th. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2009. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10546-3_25
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DOI: https://doi.org/10.1007/978-3-642-10546-3_25
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