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CIRNet: An Improved Lightweight Convolution Neural Network Architecture with Inverted Residuals for Universal Steganalysis

  • Research Article-Computer Engineering and Computer Science
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Abstract

Steganalysis techniques play a crucial role in forensic investigation, aiding professionals in identifying digital images with unsafe content. This work proposes a novel and improved lightweight deep-learning convolution neural network architecture called CIRNet, which provides universal steganalysis capabilities. CIRNet leverages inverted residual blocks integrated with a self-attention mechanism that effectively minimises the detection error rate and the computational complexity for steganalysis. The inverted residual blocks include lightweight depth-wise and point-wise convolutions supplemented by a self-attention module. This integration strengthens the saliency of feature maps corresponding to the embedding regions while reducing the network’s parameters and the total floating-point operations. Consequently, CIRNet achieves superior accuracy, making it an excellent choice for deployment on lightweight smart mobile systems. Experimental results validate the superiority of the proposed CIRNet over several state-of-the-art deep-learning networks commonly employed in steganalysis. The results further demonstrate the enhanced generalisation ability, particularly when applied to diverse datasets generated from BOSSbase 1.01 and BOWS2 using HUGO, WOW, and S-UNIWARD steganography methods with two distinct payloads. These findings establish CIRNet as an efficient and elite performer in the domain of lightweight steganalysis.

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Notes

  1. http://dde.binghamton.edu/download/

  2. BOWS2—Mendeley Data

  3. https://github.com/tensorflow/tensorflow/issues/32809

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Acknowledgements

We acknowledge meaningful discussions with Prof. R.K. Aggarwal, J.N.U., Delhi.

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  1. Department of Computer Science, The NorthCap University, Gurugram, Haryana, India

    Ankita Gupta, Rita Chhikara & Prabha Sharma

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  1. Ankita Gupta
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Correspondence to Ankita Gupta.

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Gupta, A., Chhikara, R. & Sharma, P. CIRNet: An Improved Lightweight Convolution Neural Network Architecture with Inverted Residuals for Universal Steganalysis. Arab J Sci Eng 49, 12219–12233 (2024). https://doi.org/10.1007/s13369-023-08630-x

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