Stegbreak does just that for content hidden with JSteg-Shell, JPHide or Outguess 0.13b. To verify that the detected images have hidden content, it is necessary to launch a dictionary attack against the JPEG files. Because of that, Stegdetect can not guarantee the existence of a hidden message. The statistical tests used to find steganographic content in images indicate nothing more than a likelihood that content has been embedded. Furthermore, many images downloaded from the Internet are of very low quality, while the images that were used to calibrate Stegdetect are of higher quality, because they come directly from a digital camera. Instead, it means that the detection functions for JPHide need to be improved to be more accurate. That Stegdetect finds so many images that seem to have content hidden with JPHide does not indicate that there are many images that really contain hidden content. If there were hidden content, we would expect to find more areas in the image where the extended χ 2 -test shows a positive result. When analyzing the graph, we see only a few high probability spikes. Images with monotone backgrounds like the painting in Figure 14 are more likely to be false positives. We find similar false positives when trying to detect content hidden with OutGuess. However, when analyzing the probability of embedding displayed next to the drawing, we do not see a plateau at the beginning, as we would expect had encrypted data been embedded. Stegdetect indicates that content has been hidden by JSteg. An example of a false positive is shown in Figure 13. We notice that there are special classes of images for which Stegdetect falsely indicates hidden content. Reducing it improves the “true positive” rate the best. As a result, the false positive rate is the dominating term in the denomiator. We assume that P ( S ), the percentage of images containing steganographic content, is low in comparison to P ( D |¬ S ), the percentage of false positives. There are two possible approaches: decreasing the false negative rate or decreasing the false positive rate. To improve the efficiency of our detection system,we need to increase the “true positive” rate. P ( D | S ) is the probability that we detect an image that has steganographic content and P ( D |¬ S ) the false positive rate. ( S ) is the probability of steganographic content in images and P ( ¬ S ) its complement. Options - iteration start, capital letter for 2nd dataset - iteration limit - key - filename of dataset - use error correcting encoding -p parameter passed to destination data handler -r retrieve message from data -x number of key derivations to be tried -m mark pixels that have been modified -t collect statistic information -F turns statistical steganalysis foiling on/off. Installation $ sudo aptitude install outguess Usage Syntax outguess ] The program relies on data specific handlers that will extract redundant bits and write them back after modification.Ĭurrently only the PPM, PNM, and JPEG image formats are supported, although outguess could use any kind of data, as long as a handler were provided. The nature of the data source is irrelevant to the core of outguess. Outguess is a universal steganographic tool that allows the insertion of hidden information into the redundant bits of data sources.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |