Universal Steganalysis Based on Few-shot Learning

In recent years, deep learning has shown excellent performance in image steganalysis. At present, most of the image steganalysis models based on deep learning are special steganalysis models, which are only applied to a specific steganography. To detect the stego images of other steganographic algorithms using the special steganalysis model, a large number of stego images encoded by the steganographic algorithms are regarded as datasets to retrain the model. However, in practical steganalysis tasks, it is difficult to obtain a large number of encoded stego images, and it is a great challenge to train the universal steganalysis model with very few stego image samples. Inspired by the research results in the field of few-shot learning, a universal steganalysis method is proposed based on transductive propagation network. First, the feature extraction network is improved based on the existing few-shot learning classification framework, and the multi-scale feature fusion network is designed, so that the few-shot classification model can extract more steganalysis features for the classification task based on weak information such as secret noise residue. Second, to solve the problem that steganalysis model based on few-shot learning is difficult to converge, the initial model with prior knowledge is obtained by pre-training. Then, the steganalysis models based on few-shot learning in frequency domain and spatial domain are trained respectively. The results of self-test and cross-test show that the average detection accuracy is above 80%. Furthermore, the steganalysis models based on few-shot learning in frequency domain and spatial domain are retrained by means of dataset enhancement, so that the detection accuracy of the steganalysis models based on few-shot learning is improved to more than 87% compared with the previous steganalysis model based on few-shot learning. Finally, the proposed steganalysis model based on few-shot learning is compared with the existing steganalysis models in frequency domain and spatial domain, the result shows that the detection accuracy of the universal steganalysis model based on few-shot learning is slightly below those of SRNet and ZhuNet in spatial domain and is beyond that of existing best steganalysis model in frequency domain under the experimental setup of few-shot learning. The experimental results show that the proposed method based on few-shot learning is efficient and robust for the detection of unknown steganographic algorithms. © 2022 Chinese Academy of Sciences. All rights reserved.