SPAM 기반 영상 스테그아날리시스의 범용성에 대한 실험적 검증

초록

Many steganography algorithms have been studied, and steganalysis for detecting stego images which steganography is applied to has also been studied in parallel. Especially, in the case of the image steganalysis, the features such as ALE, SPAM, and SRMQ are extracted from the statistical characteristics of the image, and stego images are classified by learning the classifier using various machine learning algorithms. However, these studies did not consider the effect of image size, aspect ratio, or message-embedding rate, and thus the features might not function normally for images with conditions different from those used in the their studies. In this paper, we analyze the classification rate of the SPAM-based image stegnalysis against variety image sizes aspect ratios and message-embedding rates and verify its versatility.

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