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.

https://northumbria-test.eprints-hosting.org/id/document/273545

Image Steganography: A Review of the Recent Advances

An encrypted coverless information hiding method based on generative models

Image steganography is a technology that hides sensitive information into an image. The traditional image steganography method tends to securely embed secret information in the host image so that the payload capacity is almost ignored and the steganographic image quality needs to be improved for the Human Visual System(HVS). Therefore, in this work, we propose a new high capacity image steganography method based on deep learning. The Discrete Cosine Transform(DCT) is used to transform the secret image, and then the transformed image is encrypted by Elliptic Curve Cryptography(ECC) to improve the anti-detection property of the obtained image. To improve steganographic capacity, the SegNet Deep Neural Network with a set of Hiding and Extraction networks enables steganography and extraction of full-size images. The experimental results show that the method can effectively allocate each pixel in the image so that the relative capacity of steganography reaches 1. Besides, the image obtained using this steganography method has higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) values, reaching 40dB and 0.96, respectively.

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A New High Capacity Image Steganography Method Combined With Image Elliptic Curve Cryptography and Deep Neural Network

Abstract Steganography is the science and art of covert communication. Conversely, steganalysis is the study of uncovering the steganographic process. The evolution of steganography has been paralleled by the development of steganalysis. In this game of hide and seek, the two player’s steganography and steganalysis always want to break the other down. Over the past three decades, research has produced a plethora of remarkable image steganography techniques (ISTs). The major challenge for most of these ISTs is to achieve a fair balance between the metrics such as high hiding capacity (HC), better imperceptibility, and improved security. This study aims to present an exhaustive scrutiny of various ISTs from the classical to recent developments in the spatial domain, with respect to various image steganographic metrics. Further, the current status, recent developments, open challenges, and promising directions in this field are also highlighted.

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Digital image steganography and steganalysis: A journey of the past three decades

Steganography and steganalysis is a relatively new-fangled scientific discipline in security systems and digital forensics, respectively, but one that has matured greatly over the past two decades. In any specialism of human endeavour, it is imperative to periodically pause and review the state of the discipline for what has been achieved till date. This article scrutinizes where the discipline of steganography and steganalysis at this point in time in context to the common user and new researchers in terms of current trends. Also, what has been accomplished in order to critically examine what has been done well and what ought to be done better. The state-of-the-art techniques for steganography and steganalysis (image and video) have been deliberated for the last 5 years literature. Further, the paper also takes stock the dataset and tools available for multimedia steganography and steganalysis with the examples where steganography has been used in real-life. It is a corpus of the author’s opinion and the viewpoints of different other researchers and practitioners, working in this discipline. Additionally, experiments were done using image steganography techniques to analyse the recent trends. This survey is intended to provide a complete guide for common people and new researchers and scholars approaching this field, sight on the existing and the future of steganography and steganalysis.

Steganography and Steganalysis (in digital forensics): a Cybersecurity guide

Traditional steganography methods often hide secret data by establishing a mapping relationship between secret data and a cover image or directly in a noisy area, but has a low embedding capacity. Based on the thought of deep learning, in this paper, we propose a new image steganography scheme based on a U-Net structure. First, in the form of paired training, the trained deep neural network includes a hiding network and an extraction network; then, the sender uses the hiding network to embed the secret image into another full-size image without any modification and sends it to the receiver. Finally, the receiver uses the extraction network to reconstruct the secret image and original cover image correctly. The experimental results show that the proposed scheme compresses and distributes the information of the embedded secret image into all available bits in the cover image, which not only solves the obvious visual cues problem, but also increases the embedding capacity.

/pdf/reversible-image-steganography-scheme-based-on-a-u-net-8d2kelosz1.pdf

Reversible Image Steganography Scheme Based on a U-Net Structure

To solve the problem of the low capacity of traditional information hiding and steganography, the deep neural network is used to construct a hidden network and a decoding network to hide one image ...

High-Capacity Information Hiding Based on Residual Network

The traditional information hiding is realized by embedding the secret information into the multimedia, but it will inevitably leave the modification mark in the carrier. This paper proposed a new method of coverless information hiding. First, the improved Wasserstein GAN (WGAN-GP) model is constructed, and the model is trained with disguised images and secret images. Then, after the model is stable, a disguised image is passed to the generator. Finally, the generator generates the image that is visually the same as the secret image, thereby achieving the same effect as transmitting the secret image. Experimental results show that this method not only has a good effect on the security of secret information transmission, but also increases the capacity of information hiding.

/pdf/a-coverless-steganography-method-based-on-generative-219jmojm1t.pdf

A coverless steganography method based on generative adversarial network

Steganalysis technology judges whether there is secret information in the carrier by monitoring the abnormality of the carrier data, so the traditional information hiding technology has reached the bottleneck. Therefore, this paper proposed the coverless information hiding based on the improved training of Wasserstein GANs (WGAN-GP) model. The sender trains the WGAN-GP with a natural image and a secret image. The generated image and secret image are visually identical, and the parameters of generator are saved to form the codebook. The sender uploads the natural image (disguise image) to the cloud disk. The receiver downloads the camouflage image from the cloud disk and obtains the corresponding generator parameter in the codebook and inputs it to the generator. The generator outputs the same image for the secret image, which realized the same results as sending the secret image. The experimental results indicate that the scheme produces high image quality and good security.

/pdf/coverless-information-hiding-based-on-wgan-gp-model-4ykdsw54fd.pdf

Baoxia Li

Papers

Reversible Image Steganography Scheme Based on a U-Net Structure

A New High Capacity Image Steganography Method Combined With Image Elliptic Curve Cryptography and Deep Neural Network

High-Capacity Information Hiding Based on Residual Network

A coverless steganography method based on generative adversarial network

Coverless Information Hiding Based on WGAN-GP Model