This paper presents a literature review of image steganography techniques in the spatial domain for last 5 years. The research community has already done lots of noteworthy research in image steganography. Even though it is interesting to highlight that the existing embedding techniques may not be perfect, the objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing up-to-date techniques for researchers that are involved in the designing of image steganographic system. In this article, the general structure of the steganographic system and classifications of image steganographic techniques with its properties in spatial domain are exploited. Furthermore, different performance matrices and steganalysis detection attacks are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.

Image steganography in spatial domain: A survey

Comprehensive Survey of Image Steganography: Techniques, Evaluations, and Trends in Future Research

Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) are two measuring tools that are widely used in image quality assessment. Especially in the steganography image, these two measuring instruments are used to measure the quality of imperceptibility. PSNR is used earlier than SSIM, is easy, has been widely used in various digital image measurements, and has been considered tested and valid. SSIM is a newer measurement tool that is designed based on three factors i.e. luminance, contrast, and structure to better suit the workings of the human visual system. Some research has discussed the correlation and comparison of these two measuring tools, but no research explicitly discusses and suggests which measurement tool is more suitable for steganography. This study aims to review, prove, and analyze the results of PSNR and SSIM measurements on three spatial domain image steganography methods, i.e. LSB, PVD, and CRT. Color images were chosen as container images because human vision is more sensitive to color changes than grayscale changes. Based on the test results found several opposing findings, where LSB has the most superior value based on PSNR and PVD get the most superior value based on SSIM. Additionally, the changes based on the histogram are more noticeable in LSB and CRT than in PVD. Other analyzes such as RS attack also show results that are more in line with SSIM measurements when compared to PSNR. Based on the results of testing and analysis, this research concludes that SSIM is a better measure of imperceptibility in all aspects and it is preferable that in the next steganographic research at least use SSIM.

PSNR vs SSIM: imperceptibility quality assessment for image steganography

Adoption of deep learning in image steganalysis is still in its initial stage. In this paper, we propose a generic hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models. Our proposed framework involves two main stages. The first stage is hand-crafted, corresponding to the convolution phase and the quantization and truncation phase of the rich models. The second stage is a compound deep-neural network containing multiple deep subnets, in which the model parameters are learned in the training procedure. We provided experimental evidence and theoretical reflections to argue that the introduction of threshold quantizers, though disabling the gradient-descent-based learning of the bottom convolution phase, is indeed cost-effective. We have conducted extensive experiments on a large-scale data set extracted from ImageNet. The primary data set used in our experiments contains 500 000 cover images, while our largest data set contains five million cover images. Our experiments show that the integration of quantization and truncation into deep-learning steganalyzers do boost the detection performance by a clear margin. Furthermore, we demonstrate that our framework is insensitive to JPEG blocking artifact alterations, and the learned model can be easily transferred to a different attacking target and even a different data set. These properties are of critical importance in practical applications.

Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework

The limited available spectrum and the inefficiency in the spectrum usage necessitate a new communication technology, referred to as cognitive radio (CR) networks. The key characteristic of CR system is that it senses the electromagnetic environment to adapt their operation and dynamically vary its radio operating parameters. A cognitive radio must detect the presence of primary user to avoid interference. Spectrum sensing helps to detect the spectrum holes (unutilized bands of the spectrum) providing high spectral resolution capability. Different spectrum sensing techniques for cognitive radio are discussed in this paper.

A Survey on Spectrum Sensing Techniques in Cognitive Radio

http://romisatriawahono.net/lecture/rm/survey/computer%20vision/Subhedar%20-%20Image%20Steganography%20-%202014.pdf

Current status and key issues in image steganography

The growing demand of wireless applications has put a lot of constraints on the usage of available radio spectrum which is limited and precious resource. However, a fixed spectrum assignment has lead to under utilisation of spectrum as a great portion of licensed spectrum is not effectively utilised. Cognitive radio is a promising technology which provides a novel way to improve utilisation efficiency of available electromagnetic spectrum. Spectrum sensing helps to detect the spectrum holes (underutilised bands of the spectrum) providing high spectral resolution capability. In this paper, survey of spectrum sensing techniques is presented. The challenges and issues involved in implementation of spectrum sensing techniques are discussed in detail giving comparative study of various methodologies.

/pdf/spectrum-sensing-techniques-in-cognitive-radio-networks-a-2tvmhricim.pdf

Spectrum sensing techniques in cognitive radio networks : a survey

Image steganography using redundant discrete wavelet transform and QR factorization

The selection of appropriate type and grade of concrete for a particular application is the critical step in any construction project. Workability and compressive strength are the two significant parameters that need special attention. This study aims to predict the slump along with 7-days & 28-days compressive strength based on the data collected from various RMC plants. There are many studies reported in general to address this issue from time to time over a long period. However, considering the worldwide use of a huge quantity of concrete for various infrastructure projects, there is a scope for the study that leads to most accurate estimate. Here, data from various concrete mixing plants and ongoing construction sites was collected for M20, M25, M30, M35, M40, M45, M50, M55, M60 and M70 grade of concrete. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were built to predict slump as well as 7-days and 28-days compressive strength. A variety of experiments was carried out that suggests ANN performs better and yields more accurate prediction compared to MLR model for both slump & compressive strength.

http://www.jsoftcivil.com/article_59743_69ad1d847a160a4a1b91cf459ce77650.pdf

Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network

In this paper, we present curvelet transform (CT) based image steganography that embeds scrambled secret image in appropriately selected cover image. Curvelet transform offers optimal nonadaptive sparse representation of objects with edges and possesses high directional sensitivity and anisotropy. Cover image is decomposed using curvelet transform and adaptive block based embedding is carried out only in non-uniform regions of high frequency curvelet coefficients. In addition, this work also demonstrates a new cover selection method to choose suitable cover from image database. Spatial information based image complexity is modelled using fuzzy logic to identify set of images that yields least detectable stego image. From this set of ranked images, best cover can be chosen for carrying secret information depending on amount of information to be embedded. Cover selection offers reduced risk of detectability and ensures security. It is evident from experimental results that proposed method outperforms conventional methods in terms of imperceptibility, robustness and security.

Mansi S. Subhedar

Papers

Current status and key issues in image steganography

Spectrum sensing techniques in cognitive radio networks : a survey

Image steganography using redundant discrete wavelet transform and QR factorization

Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network

Curvelet transform and cover selection for secure steganography