Image Steganography: A Review of the Recent Advances
TLDR
Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Networks-based methods as mentioned in this paper.Abstract:
Image Steganography is the process of hiding information which can be text, image or video inside a cover image. The secret information is hidden in a way that it not visible to the human eyes. Deep learning technology, which has emerged as a powerful tool in various applications including image steganography, has received increased attention recently. The main goal of this paper is to explore and discuss various deep learning methods available in image steganography field. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field.read more
Citations
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End-to-End Image Steganography Using Deep Convolutional Autoencoders
TL;DR: In this paper, a light-weight yet simple deep convolutional autoencoder architecture is proposed to embed a secret image inside a cover image as well as to extract the embedded secret image from the stego image.
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Double-Matrix Decomposition Image Steganography Scheme Based on Wavelet Transform with Multi-Region Coverage
TL;DR: Experimental results show that the proposed double-matrix decomposition image steganography scheme with multi-region coverage has excellent performance in concealment and quality of extracted secret images, and secret information is extracted from steganographic images attacked by various image processing attacks, which proves that the method has good anti-attack ability under different attacks.
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Digital image steganography survey and investigation (goal, assessment, method, development, and dataset)
TL;DR: In this paper , the authors classify image steganography research based on its goals and its assessment tools, and review the use of assessment tools in depth because it is closely related to the goal of steganographic research, including challenges or issues, methods, developments, and popular datasets.
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