Abstract
A mechanism for detecting Open Space Steganography (OSS) is described founded on the observation that the length of white space segments increases in the presence of OSS. The frequency of white space segments of different length is conceptualized in terms of an n-dimensional feature. This feature space is used to encode webpages (labelled as OSS or not OSS) so that each page is represented in terms of a feature vector. This representation was used to train a classifier which can subsequently be used to detect the presence, or otherwise, of OSS in unseen webpages. The proposed approach is evaluated using a number of different classifiers and with and without feature selection. Its operation is also compared with two existing OSS detection approaches. From the evaluation a best accuracy of \(96.7\%\) was obtained. The evaluation also demonstrated that the proposed method outperforms the two alternative techniques by a significant margin.
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Sedeeq, I., Coenen, F., Lisitsa, A. (2017). A Prediction Model Based Approach to Open Space Steganography Detection in HTML Webpages. In: Kraetzer, C., Shi, YQ., Dittmann, J., Kim, H. (eds) Digital Forensics and Watermarking. IWDW 2017. Lecture Notes in Computer Science(), vol 10431. Springer, Cham. https://doi.org/10.1007/978-3-319-64185-0_18
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DOI: https://doi.org/10.1007/978-3-319-64185-0_18
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