High-Pass-Kernel-Driven Content-Adaptive Image Steganalysis Using Deep Learning

Saurabh Agarwal, Hyenki Kim and Ki-Hyun Jung ()
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Saurabh Agarwal: Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida 201313, India
Hyenki Kim: Department of Software Convergence, Andong National University, Andong 36729, Republic of Korea
Ki-Hyun Jung: Department of Software Convergence, Andong National University, Andong 36729, Republic of Korea

Mathematics, 2023, vol. 11, issue 20, 1-12

Abstract: Digital images cannot be excluded as part of a popular choice of information representation. Covert information can be easily hidden using images. Several schemes are available to hide covert information and are known as steganography schemes. Steganalysis schemes are applied on stego-images to assess the strength of steganography schemes. In this paper, a new steganalysis scheme is presented to detect stego-images. Predefined kernels guide the set of a conventional convolutional layer, and the tight cohesion provides completely guided training. The learning rate of convolutional layers with predefined kernels is higher than the global learning rate. The higher learning rate of the convolutional layers with predefined kernels assures adaptability according to network training, while still maintaining the basic attributes of high-pass kernels. The Leaky ReLU layer is exploited against the ReLU layer to boost the detection performance. Transfer learning is applied to enhance detection performance. The deep network weights are initialized using the weights of the trained network from high-payload stego-images. The strength of the proposed scheme is verified on the HILL, Mi-POD, S-UNIWARD, and WOW content-adaptive steganography schemes. The results are overwhelming and better than the existing steganalysis schemes.

Keywords: digital image steganography; image steganalysis; convolutional neural network; image classification; image forensic (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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