DCT domain embedding techniques are very popular due to the fact that DCT-based image format, JPEG, is widely used in the public domain in addition to being the most common output format of digital cameras. The area under the ROC curve, also known as AUR, was calculated as the accuracy of the designed classifier against previously unseen images. Given the decision values, the receiver operating curves ͑ ROCs ͒ curves are obtained. The rest of images ͑ i.e., cover and stego ͒, 90%, were tested against the designed classifier, and decision values were collected for each. Here, if the two sets of images ͑ i.e., cover and stego ͒ are nonequal, 10% of the smaller set is chosen as the size of the design set. A random subset of images, 10%, was used to train the classifier. To train and test a classifier, the following steps were performed: 1. To avoid high computational cost and to obtain a reasonable success, we have employed a linear SVM ͑ Ref. SVMs are more powerful, but on the down side, require more computational power, especially if a nonlinear kernel is employed. Two of the techniques more widely used by researchers for universal steganalysis are Fisher’s linear discriminate ͑ FLD ͒ and support vector ma- chines ͑ SVMs ͒. A number of different classifiers could be employed for this purpose. As noted earlier, the calculated features vectors obtained from each universal steganalysis technique are used to train a classifier, which in turn is used to classify between cover and stego images. In the following sections, we discuss in more detail the number of changeable coefficients with respect to the image type and the embedding technique. Note that the number of changeable coefficients in an image does not necessarily indicate the embedding rate achievable by a particular steganographic technique ͑ as discussed in Sec. In creating our data set, we use the first approach in setting the message size as it also takes into account the image ͑ content ͒ itself, unlike the latter two. Similar to the preceding, we could have two images of the same size, but with a different number of changeable coefficients. few relative changes with respect to their size and images that have maximal changes incurred during the embedding process. O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers. Get Machine Learning in Image Steganalysis now with the O’Reilly learning platform. The basis of any statistical attack is some theoretical model capturing some difference between steganograms and natural images. Sacrificing some embedding capacity, a certain fraction of the coefficients was reserved for dummy modifications designed only to even out the statistics and prevent the (then) known statistical attacks.Ī key advantage of machine learning over statistical attacks is that it does not automatically reveal a statistical model to the designer. Most instructively, Outguess 0.2 introduced so-called statistics-aware embedding. For each and every one, new embedding algorithms have emerged, specifically avoiding the artifact detected. None of the statistical attacks are difficult to counter. The most well-known example is the pairs-of-values or χ 2 test, which we discussed in Section 2.3.4. ![]() ![]() These techniques were targeted and aimed to exploit specific artifacts caused by specific embedding algorithms. The histogram was much used in statistical steganalysis in the early days, both in the spatial and the JPEG domains. Subsequent chapters will make use of these techniques in other domains. In this chapter, we will discuss a range of general concepts and techniques using the histogram in the spatial domain. Analysis of the histogram has been used in steganalysis since the beginning, from the simple pairs-of-values or χ 2 test to analysis of the histogram characteristic function (HCF) and second-order histograms.
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