Steganography is conducive to communication security, but the abuse of steganography brings many potential dangers. And then, steganalysis plays an important role in preventing the abuse of steganography. Nowadays, steganalysis based on deep learning generally has a large number of parameters, and its pertinence to adaptive steganography algorithms is weak. In this article, we propose a lightweight convolutional neural network named IAS-CNN which targets to image adaptive steganalysis. To solve the limitation of manually designing residual extraction filters, we adopt the method of self-learning filter in the network. That is, a high-pass filter in spatial rich model is applied to initialize the weights of the first layer and then these weights are updated through the backpropagation of the network. In addition, the knowledge of selection channel is incorporated into IAS-CNN to enhance residuals in regions that have a high probability for steganography by inputting embedding probability maps into IAS-CNN. Also, IAS-CNN is designed as a lightweight network to reduce the consumption of resources and improve the speed of processing. Experimental results show that IAS-CNN performs well in steganalysis. IAS-CNN not only has similar performance with YedroudjNet in S-UNIWARD steganalysis but also has fewer parameters and convolutional computations.
A sensitivity analysis of probability maps in deep‐learning‐based anatomical segmentation
