Abstract
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.
Highlights
Image steganography is a technique that embeds secret information into the cover image and modifies the image content and statistical features as little as possible.[1]
Three steganography algorithms of the spatial domain, such as HUGO, WOW, and S-UNIWARD were used to evaluate the performance of the network
Hand-crafted high-pass filters are used in spatial rich model (SRM), a mature steganalysis model, to extract a variety of image residuals
Summary
Image steganography is a technique that embeds secret information into the cover image and modifies the image content and statistical features as little as possible.[1] The embedding of secret information can be accomplished in two domains: spatial domain and frequency domain. Steganography based on the spatial domain is characterized by slightly modifying the pixel values to achieve similar visual quality between cover image and steganographic image. Steganography based on the frequency domain is generally applied to JPEG images and accomplished by changing discrete cosine transform (DCT) coefficients. Least significant bits (LSB)[2] is an early spatial-domain steganography algorithm which embeds secret information into the lowest significant bit of the pixel value of the cover image. The algorithm is simple but changes the statistical features of the image. Many adaptive steganography algorithms have been proposed, such as HUGO,[3] WOW,[1] and S-UNIWARD4 in the spatial
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More From: International Journal of Distributed Sensor Networks
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