A Preprocessing Methodology by Using Additional Steganography on CNN-based Steganalysis

There exists a need of “image steganalysis” which reveals whether steganographic signals are embedded in an image to improve information security. Among various steganalysis, Convolutional Neural Networks (CNN) based steganalysis is promising since it can automatically learn the features of diverse steganographic algorithms. However, we discover the detection performance of CNN is degraded when an image is intentionally reduced by the nearest-neighbor interpolation before steganography. This is because spatial frequency in a reduced image gets high, which disturbs the training. In order to overcome this shortcoming, in this paper, we propose a preprocessing methodology by using additional steganography on CNN-based steganalysis. In the proposed preprocessing, steganographic signals are additionally embedded into both reduced original images and reduced steganographic ones since a difference of spatial frequencies between them gets obvious, which helps CNN learn features. Whenever reduced images are trained in CNN or inspected whether they are steganographic ones or not, steganography is applied to them once by the proposed preprocessing. Thus, an image is regarded as a steganographic one if the trained model judges steganography is applied to it twice; otherwise it is an original one. Since the proposed methodology is very simple, its computational cost is low. Our evaluation shows accuracy in a model with the proposed preprocessing is 10.6% higher than that in the conventional one. Besides, even in the situation where another steganography is additionally embedded, the proposed preprocessing yields 7% higher accuracy compared with the conventional one.