With the pervasive of image steganography algorithms in social media, image steganalysis becomes inevitably important nowadays. One of the most secure steganographic scheme is called content-adaptive steganography. WOW, S-UNIWARD, HUGO and HILL are all successful steganography algorithms of this kind. Since content-adaptive steganography will calculate embedding cost after they evaluate the cover image, and will tend to put more embeddings in complex regions. It makes it harder for image steganalyzers to detect if the image has been embedded information or not.

Our goal is to provide a data-driven method, which does not apply hand-crafted high-pass filtering in preprocess step or any neural network based architectures. We have unsupervised feature extraction and machine learning-based classifier to fulfill the task. Specifically, we first split input image into 3×3 blocks, and partition blocks into several groups based their embedding cost. We use Saab transform to extract features on blocks and make decision. The difference of soft decision scores on cover image and stego image are efficient for us to do image-wise decision. In order to find the embed locations in unseen images, we train an embed location classifier from block soft decision scores, as shown in Fig. 1. Based on embed location probability score from each group, we train the final image-wise ensemble classifier and give us the image-level decision, as shown in Fig.2 .

Compared to CNN-based steganalysis models, our method does not use end-to-end training and backward propagation. Therefore, it is very light-weight in terms of model size and memory usage. In the meantime, our method can beat all traditional steganalysis method and some benchmarking CNN-based model.


— by Yao Zhu