Steganalysis using color wavelet statistics and one-class support vector machines
Security, steganography, and watermarking of multimedia contents VI, 2004•spiedigitallibrary.org
Steganographic messages can be embedded into digital images in ways that are
imperceptible to the human eye. These messages, however, alter the underlying statistics of
an image. We previously built statistical models using first-and higher-order wavelet
statistics, and employed a non-linear support vector machines (SVM) to detect
steganographic messages. In this paper we extend these results to exploit color statistics,
and show how a one-class SVM greatly simplifies the training stage of the classifier.
imperceptible to the human eye. These messages, however, alter the underlying statistics of
an image. We previously built statistical models using first-and higher-order wavelet
statistics, and employed a non-linear support vector machines (SVM) to detect
steganographic messages. In this paper we extend these results to exploit color statistics,
and show how a one-class SVM greatly simplifies the training stage of the classifier.
Steganographic messages can be embedded into digital images in ways that are imperceptible to the human eye. These messages, however, alter the underlying statistics of an image. We previously built statistical models using first-and higher-order wavelet statistics, and employed a non-linear support vector machines (SVM) to detect steganographic messages. In this paper we extend these results to exploit color statistics, and show how a one-class SVM greatly simplifies the training stage of the classifier.
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