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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

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SSRN

SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

Double JPEG Compression Detection Based on Markov Model 18th

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Double JPEG Compression Detection Based on Markov Model

18th International Workshop on Digital Forensics and Watermarking, IWDW 2019

作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yung-Qing Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu210044 China Shanxi Key Laboratory of Network and System Security Xidian University Xi’an710071 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China State Key Laboratory of Information Security Institute Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

ISBN: (纸本)9783030435745

In this paper, a feature based on the Markov model in quaternion discrete cosine transform (QDCT) domain is proposed for double JPEG compression detection. Firstly, a given JPEG image is extracted from blocked images to obtain amplitude and three angles (ψ, φ, and θ). Secondly, when extracting the Markov features, we process the transition probability matrix with the corresponding refinement. Our proposed refinement method not only reduces redundant features, but also makes the acquired features more efficient for detection. Finally, a support vector machine (SVM) is employed for NA-DJPEG compression detection. It is well known that detecting NA-DJPEG compressed images with (Formula Presented) is a challenging task, and when the images with small size (i.e., 64 × 64), the detection will be more difficult. The experimental result indicates that our method can still achieve a high classification accuracy in this case. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

Orbital-selective effect of spin reorientation on the Dirac fermions in a non-charge-ordered kagome ferromagnet Fe3Ge

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arXiv 2023年

作者： Lou, Rui Zhou, Liqin Song, Wenhua Fedorov, Alexander Tu, Zhijun Jiang, Bei Wang, Qi Li, Man Liu, Zhonghao Chen, Xuezhi Rader, Oliver Büchner, Bernd Sun, Yujie Weng, Hongming Lei, Hechang Wang, Shancai Leibniz Institute for Solid State and Materials Research IFW Dresden Dresden01069 Germany Helmholtz-Zentrum Berlin für Materialien und Energie Albert-Einstein-Straße 15 Berlin12489 Germany Joint Laboratory "Functional Quantum Materials" BESSY II Berlin12489 Germany Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China School of Physical Science and Technology ShanghaiTech University Shanghai201210 China ShanghaiTech Laboratory for Topological Physics ShanghaiTech University Shanghai201210 China School of Information Network Security People’s Public Security University of China Beijing100038 China Institute of High-Pressure Physics School of Physical Science and Technology Ningbo University Ningbo315211 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China Institute for Solid State and Materials Physics TU Dresden Dresden01062 Germany Department of Physics Southern University of Science and Technology Shenzhen518055 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China

Kagome magnets provide a fascinating platform for the realization of correlated topological quantum phases under various magnetic ground states. However, the effect of the magnetic spin configurations on the characteristic electronic structure of the kagome lattice layer remains elusive. Here, utilizing angle-resolved photoemission spectroscopy and density functional theory calculations, we report the spectroscopic evidence for the spin-reorientation effect of a kagome ferromagnet Fe3Ge, which is composed solely of kagome planes. As the Fe moments cant from the c axis into the ab plane upon cooling, the two kinds of kagome-derived Dirac fermions respond quite differently. The one with less-dispersive bands (kz ∼ 0) containing the 3dz2 orbitals evolves from gapped into nearly gapless, while the other with linear dispersions (kz ∼ π) embracing the 3dxz/3dyz components remains intact, suggesting that the effect of spin reorientation on the Dirac fermions has an orbital selectivity. Moreover, we demonstrate that there is no signature of charge order formation in Fe3Ge, contrasting with its sibling compound FeGe, a newly established charge-density-wave kagome magnet. Copyright © 2023, The Authors. All rights reserved.

关键词： Fermions

Collaborative filtering-based recommendation system for big data

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Collaborative filtering-based recommendation system for big ...

作者： Shen, Jian Zhou, Tianqi Chen, Lina Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology School of Computer and Software Nanjing University of Information Science and Technology Nanjing China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Guangxi Key Laboratory of Cryptography and Information Security Guilin China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Guizhousheng College of Electronics and Information Guizhou China

Collaborative filtering algorithm is widely used in the recommendation system of e-commerce website, which is based on the analysis of a large number of users' historical behaviour data, so as to explore the users' interest and recommend the appropriate products to users. In this paper, we focus on how to design a reliable and highly accurate algorithm for movie recommendation. It is worth noting that the algorithm is not limited to film recommendation, but can be applied in many other areas of e-commerce. In this paper, we use Java language to implement a movie recommendation system in Ubuntu system. Benefiting from the MapReduce framework and the recommendation algorithm based on items, the system can handle large datasets. The experimental results show that the system can achieve high efficiency and reliability in large datasets. © 2020 Inderscience Enterprises Ltd.

关键词： Collaborative filtering

Underwater image enhancement via medium transmission-guided multi-color space embedding

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arXiv 2021年

作者： Li, Chongyi Anwar, Saeed Hou, Junhui Cong, Runmin Guo, Chunle Ren, Wenqi CSIRO Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China College of Computer Science Nankai University Tianjin China Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China

State key laboratory of Information security, Institute of Information Engineering, Chinese Academy of Sciences, China Underwater images suffer from color casts and low contrast due to wavelength- A nd distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted. Inspired by underwater imaging physical models, we design a medium transmission (indicating the percentage of the scene radiance reaching the camera)-guided decoder network to enhance the response of network towards quality-degraded regions. As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods. Extensive experiments demonstrate that our Ucolor achieves superior performance against state-of-the-art methods in terms of both visual quality and quantitative metrics. The code is publicly available at: Https://***/Proj ***. Copyright © 2021, The Authors. All rights reserved.

关键词： Image enhancement

DAMIA: Leveraging domain adaptation as a defense against membership inference attacks

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arXiv 2020年

作者： Huang, Hongwei Luo, Weiqi Zeng, Guoqiang Weng, Jian Zhang, Yue Yang, Anjia College of Informatin Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China

Deep Learning (DL) techniques allow one to train a model from a dataset, and the model can be adopted to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. Being a core engine of DL, models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the dataset the model trained on. Efforts have been made to hinder the attack but unfortunately, they may be subject to a major overhead or impaired usability. In this paper, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense to counter membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another related dataset, and derives a model that underlyingly extracts the features from both the original dataset and the introduced dataset. Seeing that the model is obfuscated, membership inference fails, while the extracted common features provide supports for usability. Extensive experiments have been conducted, which validates our intuition. The model counters the membership inference attacks and has a negligible footprint to the usability. Our experiment also excludes a few factors that may hinder the performance of DAMIA, providing a potential guideline that instructs the vendors and researchers to benefit from our solution in a timely manner. Copyright © 2020, The Authors. All rights reserved.

关键词： network security

Dynamics and Resonance Fluorescence from a Superconducting Artificial Atom Doubly Driven by Quantized and Classical Fields

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arXiv 2024年

作者： Ruan, Xinhui Wang, Jia-Heng He, Dong Song, Pengtao Li, Shengyong Zhao, Qianchuan Kuang, L.M. Tsai, Jaw-Shen Zou, Chang-Ling Zhang, Jing Zheng, Dongning Astafiev, O.V. Liu, Yu-Xi Peng, Zhihui Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Integrated Circuits Tsinghua University Beijing100084 China Institute of Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100190 China Center for Quantum Computing RIKEN Saitama351-0198 Japan Graduate School of Science Tokyo University of Science 1-3 Kagurazaka Shinjuku Tokyo162-0825 Japan CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China Skolkovo Institute of Science and Technology Nobel str. 3 Moscow143026 Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny141701 Russia Royal Holloway University of London Surrey EghamTW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical one by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anti-crossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches to the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric lines

Identification of important nodes on large-scale Internet based on unsupervised learning 2020

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Identification of important nodes on large-scale Internet ba...

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Fuyang Fang Daojuan Zhang Chonghua Wang De Zhang Yuanfei Zhang Chao Li Xi Luo Ye Fu Information Science Academy of China Electronics Technology Group Corporation Beijing China State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute co. Ltd Beijing China China Industrial Control Systems Cyber Emergency Response Team Beijing China Cyber space Institute of Advanced Technology Guangzhou University Guangzhou China

ISBN: (纸本)9781450387828

In recent years, scholars have conducted in-depth researches on the robustness, structural vulnerability, and detection and identification of devices in cyberspace from different perspectives such as complex networks and cyberspace resource mapping. Aiming at the problem of identifying important nodes on a large-scale Internet, a CETCRank algorithm for identifying important Internet nodes based on unsupervised learning is proposed. When the algorithm analyzes the attributes of each cyberspace equipment, it not only considers the graph structure characteristics based on the network topology, but also integrates the threat metric of cyberspace equipment. Based on the hypothesis of the cyber attack model, the effective identification of important nodes in the Internet can be realized by integrating the node attributes into the constructed Markov chain model. Experiments show that the time and space complexity of the CETCRank algorithm is suitable for analyzing large-scale Internet, and the recognition performance of important nodes is better than the PageRank algorithm.

关键词： Unsupervised Learning CETCRank Algorithm Important Nodes Large-scale Internet

Peripheral-free device pairing by randomly switching power

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arXiv 2020年

作者： Shao, Zhijian Weng, Jian Zhang, Yue Wu, Yongdong Li, Ming Weng, Jiasi Luo, Weiqi Yu, Shui College of Informatin Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China University of Technology Sydney UltimoNSW Australia

The popularity of Internet-of-Things (IoT) comes with security concerns. Attacks against wireless communication venues of IoT (e.g., Man-in-the-Middle attacks) have grown at an alarming rate over the past decade. Pairing, which allows the establishment of the secure communicating channels for IoT devices without a prior relationship, is thus a paramount capability. Existing secure pairing protocols require auxiliary equipment/peripheral (e.g., displays, speakers and sensors) to achieve authentication, which is unacceptable for low-priced devices such as smart lamps. This paper studies how to design a peripheral-free secure pairing protocol. Concretely, we design the protocol, termed SwitchPairing, via out-of-box power supplying chargers and on-board clocks, achieving security and economics at the same time. When a user wants to pair two or more devices, he/she connects the pairing devices to the same power source, and presses/releases the switch on/off button several times. Then, the press and release timing can be used to derive symmetric keys. We implement a prototype via two CC2640R2F development boards from Texas Instruments (TI) due to its prevalence. Extensive experiments and user studies are also conducted to benchmark our protocol in terms of efficiency and security. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things