| Summary: | 碩士 === 國立臺中科技大學 === 資訊工程系碩士班 === 102 === In recent years, the rapid development of the Internet has made data transfer very convenient. The use data hiding as part of terrorist movement has caused social uneasiness. However, steganalysis could be used as a way to intercept and to break possible terrorist movements. At present, most steganalysis mostly use the SVM classifier to capture feature which are then used in feature classification training and testing. The results are then used to determine a stego image or cover image. The current SVM classifier can save some computing time. In the thesis, the aim is to further reduce steganalysis detection training time and to improve the accuracy of steganalysis. The study proposed is based on Ni et al Histogram-Shifting’s which was present in 2006. The 1x4 gray value histogram of the image is scanned from left to right by a sliding window 0-255 times. The 1x4 sliding window group feature of four elements are classified, while calculating the similarity between the elements of each of the four 1x4 sliding window. An optimal solution is located from the stego image. This method does not require the benefits through SVM classifier to determine the stego image. After removing the secret information, the image can also be restored. This provide an easy and fast detection method on active Steganalysis. This study makes used of three different sizes of image gallery. Experimental results showed an accuracy detection rate of 94.13% and peak estimation rate of 99.64%. Future research includes a variety of data hiding method to provide easier and faster Steganalysis which can effectively suppress terrorist incidents.
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