Detecting Steganography Using Multi-class Classification
Document Type
Conference Proceeding
Publication Date
2007
Abstract
When a digital forensics investigator suspects that steganography has been used to hide data in an image, he must not only determine that the image contains embedded information but also identify the method used for embedding. The determination of the embedding methodor stego fingerprint — is critical to extracting the hidden information. This paper focuses on identifying stego fingerprints in JPEG images. The steganography tools targeted are F5, JSteg, Model-Based Embedding, OutGuess and StegHide. Each of these tools embeds data in a dramatically different way and, therefore, presents a different challenge to extracting the hidden information. The embedding methods are distinguished using features developed from sets of stego images that are used to train a multi-class support vector machine (SVM) classifier. For new images, the image features are calculated and evaluated based on their associated label to the most similar class, i.e., clean or embedding method feature space. The SVM results demonstrate that, in the worst case, embedding methods can be distinguished with 87% reliability. Abstract © Springer
DOI
10.1007/978-0-387-73742-3_13
Source Publication
IFIP — The International Federation for Information Processing, vol 242
Recommended Citation
Rodriguez, B., & Peterson, G. (2007). Detecting Steganography Using Multi-Class Classification. In P. Craiger & S. Shenoi (Eds.), Advances in Digital Forensics III. DigitalForensics 2007 (IFIP vol. 242, pp. 193–204). New York: Springer. https://doi.org/10.1007/978-0-387-73742-3_13
Comments
© International Federation for Information Processing 2007
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