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Latest Trends in Deep Learning Techniques for Image Steganography
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Author(s): Vijay Kumar (Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, India), Sahil Sharma (Jaypee University of Information Technology, India), Chandan Kumar (Amrita Vishwa Vidyapeetham, Amaravati, India)and Aditya Kumar Sahu (Amrita Vishwa Vidyapeetham, Amaravati, India)
Copyright: 2023
Volume: 15
Issue: 1
Pages: 14
Source title:
International Journal of Digital Crime and Forensics (IJDCF)
Editor(s)-in-Chief: Feng Liu (Chinese Academy of Sciences, China)
DOI: 10.4018/IJDCF.318666
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Abstract
The development of deep convolutional neural networks has been largely responsible for the significant strides forward made in steganography over the past decade. In the field of image steganography, generative adversarial networks (GAN) are becoming increasingly popular. This study describes current development in image steganographic systems based on deep learning. The authors' goal is to lay out the various works that have been done in image steganography using deep learning techniques and provide some notes on the various methods. This study proposed a result that could open up some new avenues for future research in deep learning based on image steganographic methods. These new avenues could be explored in the future. Moreover, the pros and cons of current methods are laid out with several promising directions to define problems that researchers can work on in future research avenues.
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