A new steganographic method using Contourlet Transform
01 Dec 2016-pp 274-278
TL;DR: This paper introduces a new steganographic method in transform domain based on contourlet PDFB (pyramidal directional filter bank) based transform that has a good imperceptibility and to show this, the method uses four different visual quality metrics.
Abstract: The technique, digital steganography is a secret data hiding scheme which involves hiding data in multimedia files. Steganographic methods embed secret data in the cover media with minimum visual and statistical distortion. This paper introduces a new steganographic method in transform domain based on contourlet PDFB (pyramidal directional filter bank) based transform. Contourlet is a multiscale and multidirectional transform domain, which provides the both spatial and frequency domain information. This method also uses the wavelet based compression technique like that of JPEG2000 on the secret data for transferring more information with fewer data. The approximation sub-band is embedded in the contourlet coefficients of the cover image. Approximation subband is multiplied by a constant factor k, which is determined experimentally. Modified values of approximation sub-band are embedded in the contourlet subband of the cover image by replacing them directly. Experimental results give that the proposed method has a good imperceptibility and to show this, the method uses four different visual quality metrics.
Citations
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19 May 2019
TL;DR: A review of relevant research in cybersecurity tools and technologies is performed, organized according to both active and passive Defensive Cyberspace Operations, which accounts for the bulk of the cyber research literature over the last two decades.
Abstract: The realm of cybersecurity is perhaps one of the most quickly evolving areas within today’s research space. New and emerging technologies, as well as the growth of cybersecurity environment...
17 citations
01 Jan 2023
TL;DR: In this article , the authors proposed a steganography method to hide the existence of the data using the image as the cover image and the parameters Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structured Similarity Index Measure (SSIM) are calculated.
Abstract: In today’s society, data security is of the utmost importance. Securing data by ensuring confidentiality and integrity is crucial. Security of information is essential for transferring confidential data, data storing, and database systems. Steganography is one of the methods of protecting data. As you can see, steganography is the art and science of concealing communication, i.e., encasing the secret message inside another medium, such as images, audio, video, or text. Steganography is a method with several advantages, including high concealment capacity and undetectability. There are different types of steganography. The type of steganography depends on the type of cover object such as text, image, video we use to conceal the secret. Image steganography is hiding the existence of the data using the image as the cover image. Lastly, the parameters Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and Structured Similarity Index Measure (SSIM) are calculated which give an indication of the efficiency of the proposed method.
References
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TL;DR: A technique for image encoding in which local operators of many scales but identical shape serve as the basis functions, which tends to enhance salient image features and is well suited for many image analysis tasks as well as for image compression.
Abstract: We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding.
6,975 citations
"A new steganographic method using C..." refers methods in this paper
...It uses Pyramidal Directional filter bank (PDFB) which consists of Laplacian Pyramidal filter [19] and a double filter band [20]....
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TL;DR: A "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information is pursued and it is shown that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves.
Abstract: The limitations of commonly used separable extensions of one-dimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches, such as curvelets, that first develop a transform in the continuous domain and then discretize for sampled data, our approach starts with a discrete-domain construction and then studies its convergence to an expansion in the continuous domain. Specifically, we construct a discrete-domain multiresolution and multidirection expansion using nonseparable filter banks, in much the same way that wavelets were derived from filter banks. This construction results in a flexible multiresolution, local, and directional image expansion using contour segments, and, thus, it is named the contourlet transform. The discrete contourlet transform has a fast iterated filter bank algorithm that requires an order N operations for N-pixel images. Furthermore, we establish a precise link between the developed filter bank and the associated continuous-domain contourlet expansion via a directional multiresolution analysis framework. We show that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing applications.
3,948 citations
TL;DR: A directionally oriented 2-D filter bank with the property that the individual channels may be critically sampled without loss of information is introduced and it is shown that these filter bank outputs may be maximally decimated to achieve a minimum sample representation in a way that permits the original signal to be exactly reconstructed.
Abstract: The authors introduce a directionally oriented 2-D filter bank with the property that the individual channels may be critically sampled without loss of information. The passband regions of the component filters are wedge-shaped and thus provide directional information. It is shown that these filter bank outputs may be maximally decimated to achieve a minimum sample representation in a way that permits the original signal to be exactly reconstructed. The authors discuss the theory for directional decomposition and reconstruction. In addition, implementation issues are addressed where realizations based on both recursive and nonrecursive filters are considered. >
911 citations
"A new steganographic method using C..." refers methods in this paper
...It uses Pyramidal Directional filter bank (PDFB) which consists of Laplacian Pyramidal filter [19] and a double filter band [20]....
[...]
TL;DR: An edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image is proposed and can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, while preserving higher visual quality of stego images at the same time.
Abstract: The least-significant-bit (LSB)-based approach is a popular type of steganographic algorithms in the spatial domain. However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. Thus the smooth/flat regions in the cover images will inevitably be contaminated after data hiding even at a low embedding rate, and this will lead to poor visual quality and low security based on our analysis and extensive experiments, especially for those images with many smooth regions. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on 6000 natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.
594 citations
TL;DR: An evaluation procedure of image watermarking systems is presented and how to efficiently evaluate the watermark performance in such a way that fair comparisons between different methods are possible is shown.
Abstract: Since the early 90s a number of papers on 'robust' digital watermarking systems have been presented but none of them uses the same robustness criteria. This is not practical at all for comparison and slows down progress in this area. To address this issue, we present an evaluation procedure of image watermarking systems. First we identify all necessary parameters for proper benchmarking and investigate how to quantitatively describe the image degradation introduced by the watermarking process. For this, we show the weaknesses of usual image quality measures in the context watermarking and propose a novel measure adapted to the human visual system. Then we show how to efficiently evaluate the watermark performance in such a way that fair comparisons between different methods are possible. The usefulness of three graphs: 'attack vs. visual-quality,' 'bit-error vs. visual quality,' and 'bit-error vs. attack' are investigated. In addition the receiver operating characteristic (ROC) graphs are reviewed and proposed to describe statistical detection behavior of watermarking methods. Finally we review a number of attacks that any system should survive to be really useful and propose a benchmark and a set of different suitable images.
591 citations