![]() ![]() This is usually done with statistical analysis using advanced statistics techniques.Īttacks and analysis on hidden information may take several forms: detecting, extracting, and disabling or destroying hidden information. The steganalyst starts by reducing the set of suspect information streams to a subset of most likely altered information streams. Unlike cryptanalysis, where it is evident that intercepted encrypted data contains a message, steganalysis generally starts with several suspect information streams but uncertainty whether any of these contain hidden message. Unless it is possible to fully recover, decrypt and inspect the hidden data, often one has only a suspect information stream and cannot be sure that it is being used for transporting secret information.Some of the suspect signal or file may have noise or irrelevant data encoded into them (which can make analysis very time consuming).The hidden data, if any, may have been encrypted before inserted into the signal or file.The suspect information stream, such as a signal or a file, may or may not have hidden data encoded into them. ![]() The goal of steganalysis is to identify suspected information streams, determine whether or not they have hidden messages encoded into them, and, if possible, recover the hidden information. It is the art of discovering and rendering useless covert messages. Steganalysis is "the process of detecting steganography by looking at variances between bit patterns and unusually large file sizes". Vapnik, V.: Statistical Learning Theory.Steganalysis is a relatively new research discipline with few articles appearing before the late-1990s. Maurer, U.M.: A Universal Statistical Test for Random Bit Generators. IEEE Transaction on Communications Com-31(6) (June 1983) Reininger, R.C., Gibson, J.D.: Distribution of the Two-Dimensional DCT Coefficients for Images. Provos, N., Honeyman, P.: Detecting Steganographic Contend on the Internet. Westfeld, A., Pfitmann, A.: Attacks on Steganographic Systems. Provos, N.: Defending Against Statistical Steganalysis. Westfeld, A.: High Capacity Despite Better Steganalysis (F5-A Stegnographic Algorithm). Johnson, N.F.: Steganography Tools and Software, This process is experimental and the keywords may be updated as the learning algorithm improves. These keywords were added by machine and not by the authors. The results of experiments show that our method can detect the hiding by Jsteg and OutGuess either. We test our method on Jsteg and OutGuess. With the statistical mode, we can discriminate the stego-images from the clear ones. Using the statistical tests as the features, we train ε -support vector regression ( ε -SVR) with train images to get the statistical mode of the estimation of the embed secrets. We find, as the increase of the embedded secrets, the randomness of the LSB sequence increase. The randomness the LSB sequence is measured by some statistical tests. So the LSB sequence of the DCT coefficients is not random as a pseudo-random sequence. The method is based on the thought that the DCT coefficients are correlative. In this paper, we provide a steganalysis method which can detect the hiding in the least significant bit of the DCT coefficients. ![]()
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