The Experts below are selected from a list of 56226 Experts worldwide ranked by ideXlab platform
Bingwen Feng - One of the best experts on this subject based on the ideXlab platform.
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Secure Binary Image Steganography Based on Fused Distortion Measurement
IEEE Transactions on Circuits and Systems for Video Technology, 2019Co-Authors: He Liyu, Yuileong Yeung, Yingjie Xue, Hongmei Liu, Bingwen FengAbstract:Some state-of-the-art Binary Image steganographic methods aim to generate stego Images with good visual quality, while others focus more on the statistical security of the anti-steganalysis. This paper proposes a Binary steganographic scheme that improves both of them by selecting more appropriate flipped pixels. First, a fused distortion measurement is developed that combines the advantages of flipping distortion measurement (FDM) and two data-carrying pixel location methods, including the edge adaptive grid method (EAG) and the “Connectivity Preserving” criterion (CPc). The FDM measures the distortion score by statistical features and achieves high-statistical security, while the EAG and CPc select pixels by analyzing the local texture structures based on visual quality. Then, to eliminate the interference brought by adjacent flipped pixels, a flipping position optimization strategy is proposed to find better positions for flipping pixels to further improve the steganographic performance. Experimental results have demonstrated that the proposed steganographic scheme can achieve stronger statistical security with better visual quality without degrading the embedding capacity.
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secure Binary Image steganography based on minimizing the distortion on the texture
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Bingwen Feng, Wei LuAbstract:Most state-of-the-art Binary Image steganographic techniques only consider the flipping distortion according to the human visual system, which will be not secure when they are attacked by steganalyzers. In this paper, a Binary Image steganographic scheme that aims to minimize the embedding distortion on the texture is presented. We extract the complement, rotation, and mirroring-invariant local texture patterns (crmiLTPs) from the Binary Image first. The weighted sum of crmiLTP changes when flipping one pixel is then employed to measure the flipping distortion corresponding to that pixel. By testing on both simple Binary Images and the constructed Image data set, we show that the proposed measurement can well describe the distortions on both visual quality and statistics. Based on the proposed measurement, a practical steganographic scheme is developed. The steganographic scheme generates the cover vector by dividing the scrambled Image into superpixels. Thereafter, the syndrome-trellis code is employed to minimize the designed embedding distortion. Experimental results have demonstrated that the proposed steganographic scheme can achieve statistical security without degrading the Image quality or the embedding capacity.
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Binary Image steganalysis based on pixel mesh markov transition matrix
Journal of Visual Communication and Image Representation, 2015Co-Authors: Bingwen Feng, Wei SunAbstract:The proposed scheme measures the distortion on the textural consistency.The textural structure dependence is organized as the Markov transition of pixel meshes.The proposed scheme well detects state-of-the-art Binary Image stego systems. Binary Image stego systems have already been well developed, which raises the requirement of a steganalytic method that detects these stego systems reliably. In this paper, a steganalytic method based on the pixel mesh Markov transition matrix (PMMTM) is presented to detect Binary Image steganography in the spatial domain. The proposed scheme measures the embedding distortion on the texture consistency. Further, the dependence among texture structures is organized as the Markov transition of pixel meshes. The final dimensionality-reduced feature set is formed by shrinking the obtained PMMTM according to its detection performance on the embedding simulators, which are developed to simulate practical stego systems. In the end, experimental results are reported, demonstrating that the proposed approach can effectively and reliably detect state-of-the-art Binary Image stego systems.
Wei Lu - One of the best experts on this subject based on the ideXlab platform.
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secure Binary Image steganography based on minimizing the distortion on the texture
IEEE Transactions on Information Forensics and Security, 2015Co-Authors: Bingwen Feng, Wei LuAbstract:Most state-of-the-art Binary Image steganographic techniques only consider the flipping distortion according to the human visual system, which will be not secure when they are attacked by steganalyzers. In this paper, a Binary Image steganographic scheme that aims to minimize the embedding distortion on the texture is presented. We extract the complement, rotation, and mirroring-invariant local texture patterns (crmiLTPs) from the Binary Image first. The weighted sum of crmiLTP changes when flipping one pixel is then employed to measure the flipping distortion corresponding to that pixel. By testing on both simple Binary Images and the constructed Image data set, we show that the proposed measurement can well describe the distortions on both visual quality and statistics. Based on the proposed measurement, a practical steganographic scheme is developed. The steganographic scheme generates the cover vector by dividing the scrambled Image into superpixels. Thereafter, the syndrome-trellis code is employed to minimize the designed embedding distortion. Experimental results have demonstrated that the proposed steganographic scheme can achieve statistical security without degrading the Image quality or the embedding capacity.
Shi Yunqing - One of the best experts on this subject based on the ideXlab platform.
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reversible Binary Image data hiding by histogram modification
Journal of Computer Applications, 2009Co-Authors: Shi YunqingAbstract:A novel reversible Binary Image data hiding scheme using Run-Length(RL) histogram modification was presented.Select the sequence of RL couples in which the length of couple is not shorter than threshold T1: First eliminate the white RL of isolated white pixels,and then embed data in the black RL whose length equals T.The proposed scheme worked for text,graphics,their mixture,and both halftone and non-halftone Binary Images.Experimental results have shown its superiority to other methods.
Koji Nakano - One of the best experts on this subject based on the ideXlab platform.
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implementations of a parallel algorithm for computing euclidean distance map in multicore processors and gpus
International journal of networking and computing, 2011Co-Authors: Duhu Man, Yasuaki Ito, Kenji Uda, Hironobu Ueyama, Koji NakanoAbstract:Given a 2-D Binary Image of size n × n, Euclidean Distance Map (EDM) is a 2-D array of the same size such that each element is storing the Euclidean distance to the nearest black pixel. It is known that a sequential algorithm can compute the EDM in O(n 2 ) and thus this algorithm is optimal. Also, work-time optimal parallel algorithms for shared memory model have been presented. However, the presented parallel algorithms are too complicated to implement in existing shared memory parallel machines. The main contribution of this paper is to develop a simple parallel algorithm for the EDM and implement it in two different parallel platforms: multicore processors and Graphics Processing Units (GPUs). We have implemented our parallel algorithm in a Linux server with four Intel hexad-core processors (Intel Xeon X7460 2.66GHz). We have also implemented it in the following two modern GPU systems, Tesla C1060 and GTX 480, respectively. The experimental results have shown that, for an input Binary Image with size of 9216 × 9216, our implementation in the multicore system achieves a speedup factor of 18 over the performance of a sequential algorithm using a single processor in the same system. Meanwhile, for the same input Binary Image, our implementation on the GPU achieves a speedup factor of 26 over the sequential algorithm implementation.
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implementations of parallel computation of euclidean distance map in multicore processors and gpus
International Conference on Networking and Computing, 2010Co-Authors: Duhu Man, Yasuaki Ito, Kenji Uda, Hironobu Ueyama, Koji NakanoAbstract:Given a 2-D Binary Image of size $n \times n$, Euclidean Distance Map (EDM) is a 2-D array of the same size such that each element is storing the Euclidean distance to the nearest black pixel. It is known that a sequential algorithm can compute the EDM in $O(n^2)$ and thus this algorithm is optimal. Also, work-time optimal parallel algorithms for shared memory model have been presented. However, these algorithms are too complicated to implement in existing shared memory parallel machines. The main contribution of this paper is to develop a simple parallel algorithm for the EDM and implement it in two parallel platforms: multicore processors and a Graphics Processing Unit (GPU). More specifically, we have implemented our parallel algorithm in a Linux server with four Intel hexad-core processors (Intel Xeon X7460 2.66GHz). We have also implemented it in a modern GPU system, Tesla C1060, respectively. The experimental results have shown that, for an input Binary Image with size of $10000\times 10000$, our implementation in the multi-core system achieves a speedup factor of 18 over the performance of a sequential algorithm using a single processor in the same system. Meanwhile, for the same input Binary Image, our implementation on the GPU achieves a speedup factor of 5 over the sequential algorithm implementation.
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fm screening by the local exhaustive search with hardware acceleration
International Journal of Foundations of Computer Science, 2005Co-Authors: Yasuaki Ito, Koji NakanoAbstract:The main contribution of this paper is to show a new approach for FM screening which we call Local Exhaustive Search (LES) method, and to present ways to accelerate the computation using an FPGA. FM screening, as opposed to conventional AM screening, keeps unit dot size when converting an original gray-scale Image into the Binary Image for printing. FM screening pays great attention to generate moire-free Binary Images reproducing continuous-tone and fine details of original photographic Images. Our basic approach for FM screening is to generate a Binary Image whose projected Image onto human eyes is very close to the original Image. The projected Image is computed by applying a Gaussian filter to the Binary Image. LES performs an exhaustive search for each of the small square subImages in the Binary Image and replaces the subImage by the best Binary pattern. The exhaustive search is repeated until no more improvement is possible. The experimental results show that LES produces a high quality and sharp Binary Image. We also implemented LES on an FPGA to accelerate the computation and achieved a speedup factor of up to 51 over the software implementations.
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fm screening by the local exhaustive search with hardware acceleration
International Parallel and Distributed Processing Symposium, 2004Co-Authors: Yasuaki Ito, Koji NakanoAbstract:Summary form only given. The main contribution of this paper is to show a new approach for FM screening which we call local exhaustive search (LES) method, and to present ways to accelerate the computation using an FPGA. FM screening, as opposed to conventional AM screening, keeps unit dot size when converting an original gray-scale Image into the Binary Image for printing. FM screening pays great attention to generate moire-free Binary Images reproducing continuous-tone and fine details of original photographic Images. Our basic approach for FM screening is to generate a Binary Image whose projected Image onto human eyes is very close to the original Image. The projected Image is computed by applying a Gaussian filter to the Binary Image. LES performs an exhaustive search for each of the small square subImages in the Binary Image and replaces the subImage by the best Binary pattern. The exhaustive search is repeated until no more improvement is possible. The experimental results show that LES produces high quality sharp Binary Images. We also implemented LES on an FPGA to accelerate the computation and achieved a speedup factor of up to 43 over the software implementations.
Yingjie Xue - One of the best experts on this subject based on the ideXlab platform.
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Secure Binary Image Steganography Based on Fused Distortion Measurement
IEEE Transactions on Circuits and Systems for Video Technology, 2019Co-Authors: He Liyu, Yuileong Yeung, Yingjie Xue, Hongmei Liu, Bingwen FengAbstract:Some state-of-the-art Binary Image steganographic methods aim to generate stego Images with good visual quality, while others focus more on the statistical security of the anti-steganalysis. This paper proposes a Binary steganographic scheme that improves both of them by selecting more appropriate flipped pixels. First, a fused distortion measurement is developed that combines the advantages of flipping distortion measurement (FDM) and two data-carrying pixel location methods, including the edge adaptive grid method (EAG) and the “Connectivity Preserving” criterion (CPc). The FDM measures the distortion score by statistical features and achieves high-statistical security, while the EAG and CPc select pixels by analyzing the local texture structures based on visual quality. Then, to eliminate the interference brought by adjacent flipped pixels, a flipping position optimization strategy is proposed to find better positions for flipping pixels to further improve the steganographic performance. Experimental results have demonstrated that the proposed steganographic scheme can achieve stronger statistical security with better visual quality without degrading the embedding capacity.
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Binary Image steganalysis based on local texture pattern
Journal of Visual Communication and Image Representation, 2018Co-Authors: Jialiang Chen, Yuileong Yeung, Yanmei Fang, Xianjin Liu, Yingjie XueAbstract:Abstract In this paper, we propose a novel steganalytic scheme based on local texture pattern (LTP) to detect Binary Image steganography. We first assess how the expanded LTPs capture embedding distortions exactly. Considering curse of dimensionality when expanding LTPs, we employ Manhattan distance to measure the pixels correlation in a 5 × 5 sized block and select the pixels with closely correlation to remove some LTPs that are not interested. Although the stego Image can maintain good visual quality, steganography scheme changes the inter-pixels correlation of Binary Image. Therefore we utilize totally 8192 LTPs histogram to define a 8192-dimensional steganalytic feature set. Original Images and stego Images are classified by ensemble classifier. Experimental results show that the proposed steganalytic method can more effectively detect state-of-the-art Binary Image steganography schemes compared with other steganalytic schemes.