Image Steganography: A Review of the Recent Advances
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Adaptive spatial image steganography and steganalysis using perceptual modelling and machine learning
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- Xie, Guoliang
- Strathclyde Thesis Copyright
- University of Strathclyde
- Doctoral (Postgraduate)
- Doctor of Philosophy (PhD)
- Centre for Excellence in Signal Image Processing
- Department of Electronic and Electrical Engineering
- Image steganography is a method for communicating secret messages under the cover images. A sender will embed the secret messages into the cover images according to an algorithm, and then the resulting image will be sent to the receiver. The receiver can extract the secret messages with the predefined algorithm. To counter this kind of technique, image steganalysis is proposed to detect the presence of secret messages. After many years of development, current image steganography uses the adaptive algorithm for embedding the secrets, which automatically finds the complex area in the cover source to avoid being noticed. Meanwhile, image steganalysis has also been advanced to universal steganalysis, which does not require the knowledge of the steganographic algorithm. With the development of the computational hardware, i.e., Graphical Processing Units (GPUs), some computational expensive techniques are now available, i.e., Convolutional Neural Networks (CNNs), which bring a large improvement in the detection tasks in image steganalysis. To defend against the attacks, new techniques are also being developed to improve the security of image steganography, these include designing more scientific cost functions, the key in adaptive steganography, and generating stego images from the knowledge of the CNNs. Several contributions are made for both image steganography and steganalysis in this thesis. Firstly, inspired by the Ranking Priority Profile (RPP), a new cost function for adaptive image steganography is proposed, which uses the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in the design. The RPP mainly includes three rules, i.e., the Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, the Spreading rule is followed to smooth the resulting image produced by 2D-SSA with WMF. The proposed algorithm has improved performance over four benchmarking approaches against non-shared selection channel attacks. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. The approach is much faster than other model-based methods. Secondly, for image steganalysis, to tackle more complex datasets that are close to the real scenarios and to push image steganalysis further to real-life applications, an Enhanced Residual Network with self-attention ability, i.e., ERANet, is proposed. By employing a more mathematically sophisticated way to extract more effective features in the images and the global self-Attention technique, the ERANet can further capture the stego signal in the deeper layers, hence it is suitable for the more complex situations in the new datasets. The proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets have demonstrated the effectiveness of the proposed methodology. Lastly, for image steganography, with the knowledge from the CNNs, a novel postcost-optimization algorithm is proposed. Without modifying the original stego image and the original cost function of the steganography, and no need for training a Generative Adversarial Network (GAN), the proposed method mainly uses the gradient maps from a well-trained CNN to represent the cost, where the original cost map of the steganography is adopted to indicate the embedding positions. This method will smooth the gradient maps before adjusting the cost, which solves the boundary problem of the CNNs having multiple subnets. Extensive experiments have been carried out to validate the effectiveness of the proposed method, which provides state-of-the-art performance. In addition, compared to existing work, the proposed method is effcient in computing time as well. In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages. For image steganalysis, a new CNN architecture has also been proposed, which utilizes multiple techniques for providing state of-the-art performance. Future directions are also discussed for indicating potential research.
- Marshall, Stephen
- Ren, Jinchang
- Doctoral thesis
- 10.48730/w7n7-r940
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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 ...
A generative steganography method based on wgan-gp. In International Conference on Artificial Intelligence and Security. Springer, 386--397. Google Scholar Cross Ref; Qiang Liu, Xuyu Xiang, Jiaohua Qin, Yun Tan, Junshan Tan, and Yuanjing Luo. 2020. Coverless steganography based on image retrieval of DenseNet features and DWT sequence mapping.
In image steganography, the statistical model of an image is crucial for hiding information in less detectable pixels and attaining better protection. This paper aims to present a systematic review of image steganography approaches. Articles are selected from reputable databases such as IEEE Explore, Web of Science (WOS), ACM, and Willey. ...
In short, this thesis has made three major contributions to image steganography and steganalysis by using perceptual modelling and machine learning. A novel cost function and a post-cost-optimization function have been proposed for adaptive spatial image steganography, which helps protect the secret messages.
Image steganography is the art of secret communication in order to exchange a secret message. In the other hand, image steganalysis attempts to detect the presence of a hidden message by searching ...
My thesis initiates the study of steganography from a cryptographic point of view. We give a precise model of a communication channel and a rigorous de nition of steganographic security, and prove that relative to a channel oracle, secure steganography exists if and only if one-way functions exist. We give tightly matching upper and lower ...
Digital image steganography is the process of embedding information withina cover image in a secure, imperceptible, and recoverable way.The three main methods of digital image steganography are spatial, transform, and neural network methods. Spatial methods modify the pixel valuesof an image to embed information, while transform methods embed hidden information within the frequency of the ...
Steganography by deep learning. In Simmons' founding paper [66], steganography and steganalysis are defined as a 3-player game. The steganographers, usually named Alice and Bob, want to exchange a message without being suspected by a third party. They must use a harmless medium, such as an image, and hide the message in this medium.
Palette based steganography: Palette based steganography [107] used palette-based images as the cover media. Image formats such as PNG, GIF and TIFF are suitable for this approach. A secret key is used to generate pseudo random numbers and the selected secret data bit gets engraved on a single cover pixel. Instead of original colour, the colour ...
Thesis submitted to the Indian Institute of Technology Guwahati for the award of the degree of Doctor of Philosophy in Computer Science and Engineering ... Indian Institute of Technology Guwahati Feb, 2021. Abstract Steganography is an art of covert communication where a message is hidden in some natural-looking objects such that no one can ...
A thesis submitted for the degree of Doctor of Philosophy Trinity 2016. Abstract The goal of steganography, the art of hiding information, is to send hidden messages ... steganography, and certain methods result in the receiver not knowing which objects contain payload. The result in the literature is severe restrictions on linguistic em-
Image steganography is the art of concealing secret information in images in a way that is imperceptible to unauthorized parties. Recent advances show that is possible to use a fixed neural network (FNN) for secret embedding and extraction. ... Digital image watermarking in the wavelet transform domain. Master's Thesis, Department of Scientific ...
Image steganography can be broadly classified into spatial domain, transform domain, spread spectrum and model based steganography as depicted in Fig. 4.In spatial domain, secret message is embedded in pixel value directly whereas transform domain methods achieve embedding by first transforming the image from spatial to frequency domain using any one of the transforms such as discrete cosine ...
Steganography is the art and science of hiding the data in some cover media like image file, audio file, video file, text file etc. Out of the various cover media available image file is the most ...
In this thesis, steganography algorithms are developed for spatial, frequency and compressed do-main, following the footpaths of the prior researchers. The presented work is an effort to provide efficient algorithms having high embedding capacity while maintaining the characteristics of carrier iv.
This paper intends to give an overview of image steganography, its uses and techniques, and attempts to identify the requirements of a good steganographic algorithm and briefly reflects on which Steganographic techniques are more suitable for which applications. : Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many ...
Steganalysis is the study of detecting secret information hidden in objects such as images, videos, texts, time series and games via steganography. Among those objects, the image is the most widely used object to hide secret messages. Detection of possible secret information hidden in images has attracted a lot of attention over the past ten years. People may conduct covert communications by ...
honors thesis uses steganography within the source address fields of Internet Protocol Version 6 (IPv6) packets to create a covert channel through which clandestine messages are passed from one party to another. A fully functional computer program was designed and written that transparently embeds messages into the source address fields of packets
Steganography is a technique that hides the secret data in the digital data, such as images, texts, audios or videos. In this thesis, we propose three steganography schemes with high embedding capacity. The first scheme that incorporates both the run-length concept and the modulus operation is to hide the secret data in the gray-level images.
Steganography is a security method that hides secret data inside cover media where the very existence of the embedded secret data is not perceptible. The cover object can be image, audio or video; the most commonly used is an image file. This thesis presents a model for protecting the security and integrity of secret data
Steganography is a technique that fits into a broader category of hiding confidential information inside various forms of media. This method offers several benefits, including the ability to store ...
This technique is the most v aluable as steganography becomes a part of the image. and it's based on embedding the data by image marking. As the secret data will. be hidden in the part of the ...
In today's interconnected world, safeguarding digital data's confidentiality and security is crucial. Cryptography and steganography are two primary methods used for information security. While these methods have diverse applications, there is ongoing exploration into the potential benefits of merging them. This review focuses on journal articles from 2010 onwards and conference papers from ...
The same image viewed by white, blue, green, and red lights reveals different hidden numbers. Steganography (/ ˌ s t ɛ ɡ ə ˈ n ɒ ɡ r ə f i / ⓘ STEG-ə-NOG-rə-fee) is the practice of representing information within another message or physical object, in such a manner that the presence of the information is not evident to human inspection.In computing/electronic contexts, a computer ...