#Outguess orginial code#

The performance of the universal steganalysis techniques, in terms of AUR, are given in Fig.

#Outguess orginial code#

The code for Outguess is publicly avail- 17 able and implemented quite efficiently in C. We embedded messages of length 0.05, 0.1, and 0.2 13 BPNZ-DCT in our cover data set using the Outguess embedding technique. Provos, proposed a solution in which some of the DCT coefficients are left unchanged in the embedding process so that following the embedding, the remaining coefficients are modified to preserve the original histogram of the DCT coefficients. Note that at the time Outguess was proposed, one of its goals was to overcome steganalysis attacks that look at changes in the DCT histograms after embedding. First, it identifies the redun- dant DCT coefficients that have minimal effect on the cover image, and then depending on the information obtained in the first step, chooses bits in which it would embed the message. Outguess, proposed by Provos realizes the embedding process in two separate steps. In the rest of this section we introduce the results obtained for each of the mentioned embedding techniques. More specifically we have used embedding rates of 0.05, 0.1, 0.2, 0.4, and 0.6 BPNZ-DCT. Note that since these techniques modify only nonzero DCT coefficients, message lengths are defined with respect to the number of nonzero DCT coefficients in the images. 14 ͒, model based, 15 and per- turbed quantization 16 ͑ PQ ͒ embedding techniques. We studied four of these methods, namely Outguess, 13 F5 ͑ Ref. Various steganographic embedding methods are proposed, with the purpose of minimizing the statistical artifacts introduced to DCT coefficients. Although modifications of properly selected DCT coefficients during embedding will not cause noticeable visual artifacts, they will nevertheless cause detectable statistical changes. 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.