Bit-o-steg detection
Due to the complexity of bit-o-steg, we turned to previous research to find a viable detection method. Each entry in the 8x8 blocks has a specific probability distribution. The distribution is found by looking at the values of that entry slot across the entire image. Figure 1 shows a histogram of an entry without data. The histogram looks at the DCT coefficient value and counts how often that value appears within that entry slot. Figure 2 shows a histogram of an entry with data. Comparing the two figures, there is a sudden drop around the 0 value in the histogram of an entry with data. The histogram of an entry with data also appears to smooth out.
These distributions are defined by their own characteristic functions. The bit-o-steg hiding distorts that distribution by randomly changes certain entries thus altering the function. Using the inner product, we could test for a match between the characteristic function and the suspect image’s probability distribution. Unfortunately, the distribution functions vary based on the subject of the picture. Furthermore, we lack the statistical background necessary to classify these distributions and properly identify the characteristic functions. Thus, implementing bit-o-steg detection proved to be beyond the scope of this project.
