scholarly journals Coverless image steganography based on DenseNet feature mapping

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Qiang Liu ◽  
Xuyu Xiang ◽  
Jiaohua Qin ◽  
Yun Tan ◽  
Yao Qiu
Keyword(s):  
Information Hiding ◽  
Binary Tree ◽  
State Of The Art ◽  
Image Steganography ◽  
High Dimensional ◽  
Feature Mapping ◽  
Hidden Information ◽  
Geometric Attacks ◽  
Coverless Information Hiding ◽  
Cis Methods

Abstract Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. Most existing coverless image steganography (CIS) methods achieve excellent robustness under non-geometric attacks. However, they do not perform well under some geometric attacks. Towards this goal, a CIS algorithm based on DenseNet feature mapping is proposed. Deep learning is introduced to extract high-dimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence, and then, all matched images are sent. For the receiver, the secret information can be recovered successfully by calculating the DenseNet hash sequence of the cover image. During the whole steganography process, the cover images remain unchanged. Experimental results and analysis show that the proposed scheme has better robust compared with the state-of-the-art methods under geometric attacks.

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

scholarly journals Coverless information hiding based on the generation of anime characters

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Yi Cao ◽  
Zhili Zhou ◽  
Q. M. Jonathan Wu ◽  
Chengsheng Yuan ◽  
Xingming Sun
Keyword(s):  
Information Hiding ◽  
Coverless Information Hiding

scholarly journals Efficient Rank-Based Diffusion Process with Assured Convergence

2021 ◽  
Vol 7 (3) ◽  
pp. 49
Author(s):  
Daniel Carlos Guimarães Pedronette ◽  
Lucas Pascotti Valem ◽  
Longin Jan Latecki
Keyword(s):  
Diffusion Process ◽  
Learning Strategies ◽  
State Of The Art ◽  
Representation Learning ◽  
Theoretical Background ◽  
High Dimensional ◽  
Visual Features ◽  
Learning Approaches ◽  
Previous Decade ◽  
Asymptotic Complexity

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

An encrypted coverless information hiding method based on generative models

2021 ◽  
Vol 553 ◽  
pp. 19-30
Author(s):  
Qi Li ◽  
Xingyuan Wang ◽  
Xiaoyu Wang ◽  
Bin Ma ◽  
Chunpeng Wang ◽  
...  
Keyword(s):  
Information Hiding ◽  
Generative Models ◽  
Coverless Information Hiding

scholarly journals A Coverless Information Hiding Algorithm Based on Grayscale Gradient Co-occurrence Matrix

2018 ◽  
Vol 35 (sup1) ◽  
pp. 23-33 ◽  
Author(s):  
Jianbin Wu ◽  
Yiwen Liu ◽  
Zhenwei Dai ◽  
Ziyang Kang ◽  
Saman Rahbar ◽  
...  
Keyword(s):  
Information Hiding ◽  
Occurrence Matrix ◽  
Coverless Information Hiding

scholarly journals A Robust Text Coverless Information Hiding Based on Multi-Index Method

2021 ◽  
Vol 29 (3) ◽  
pp. 899-914
Author(s):  
Lin Xiang ◽  
Jiaohua Qin ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Neal N. Xiong
Keyword(s):  
Information Hiding ◽  
Index Method ◽  
Multi Index ◽  
Coverless Information Hiding

scholarly journals Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop

2021 ◽  
Author(s):  
Matthew Nayor ◽  
Li Shen ◽  
Gary M. Hunninghake ◽  
Peter Kochunov ◽  
R. Graham Barr ◽  
...  
Keyword(s):  
Lung Disease ◽  
State Of The Art ◽  
Research Priorities ◽  
Genomic Variation ◽  
High Dimensional ◽  
Imaging Genomics ◽  
Blood Institute ◽  
National Heart Lung ◽  
Substantial Progress ◽  
Potential Impact

Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.

scholarly journals Knight Tour for Image Steganography Technique

2019 ◽  
Vol 9 (1) ◽  
pp. 1610-1613
Keyword(s):  
Growth Rate ◽  
Data Hiding ◽  
Information Hiding ◽  
High Capacity ◽  
Image Steganography ◽  
Cover Image ◽  
The Internet ◽  
Sensitive Information ◽  
Image File ◽  
Ascii Code

The growth rate of the Internet is exceeding that of any previous technology. As the Internet has become the major medium for transferring sensitive information, the security of the transferred message has now become the utmost priority. To ensure the security of the transmitted data, Image steganography has emerged out as an eminent tool of information hiding. The frequency of availability of image file is high and provides high capacity. In this paper, a method of secure data hiding in image is proposed that uses knight tour positions and further 8-queen positions in 8*8 pixel blocks.The cover image is divided into 8*8 pixel blocks and pixels are selected from each block corresponding to the positions of Knight in 8*8 chessboard starting from different pixel positions. 8-pixel values are selected from alternate knight position. Selected pixels values converted to 8-bit ASCII code and result in 8* 8 bit matrix. 8-Queen’s solution on 8*8 chessboard is applied on 8*8 bit matrix. The bits selected from 8-Queens positions and compared with 8-bit ASCII code of message characters. The proposed algorithm changes the LSB of only some of the pixels based on the above comparison. Based on parameters like PSNR and MSE the efficiency of the method is checked after implementation. Then the comparison done with some already proposed techniques. This is how, image steganography showed interesting and promising results when compared with other techniques.

scholarly journals High-Dimensional Pixel Entanglement: Efficient Generation and Certification

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 376
Author(s):  
Natalia Herrera Valencia ◽  
Vatshal Srivastav ◽  
Matej Pivoluska ◽  
Marcus Huber ◽  
Nicolai Friis ◽  
...  
Keyword(s):  
Carrying Capacity ◽  
Communication Systems ◽  
Degrees Of Freedom ◽  
State Of The Art ◽  
Quality Measurement ◽  
High Dimensional ◽  
Single Photons ◽  
Spatial Mode ◽  
Classical Communication ◽  
Entanglement Of Formation

Photons offer the potential to carry large amounts of information in their spectral, spatial, and polarisation degrees of freedom. While state-of-the-art classical communication systems routinely aim to maximize this information-carrying capacity via wavelength and spatial-mode division multiplexing, quantum systems based on multi-mode entanglement usually suffer from low state quality, long measurement times, and limited encoding capacity. At the same time, entanglement certification methods often rely on assumptions that compromise security. Here we show the certification of photonic high-dimensional entanglement in the transverse position-momentum degree-of-freedom with a record quality, measurement speed, and entanglement dimensionality, without making any assumptions about the state or channels. Using a tailored macro-pixel basis, precise spatial-mode measurements, and a modified entanglement witness, we demonstrate state fidelities of up to 94.4% in a 19-dimensional state-space, entanglement in up to 55 local dimensions, and an entanglement-of-formation of up to 4 ebits. Furthermore, our measurement times show an improvement of more than two orders of magnitude over previous state-of-the-art demonstrations. Our results pave the way for noise-robust quantum networks that saturate the information-carrying capacity of single photons.

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