通讯机构：
[Li, L ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.
关键词：
Differential power analysis;Hamming weight;Ghost peaks;AES
摘要：
Differential power analysis (DPA) is disturbed by ghost peaks. There is a phenomenon that the mean absolute difference (MAD) value of the wrong key is higher than the correct key. We propose a compressed key guessing space (CKGS) scheme to solve this problem and analyze the AES algorithm. The DPA based on this scheme is named CKGS-DPA. Unlike traditional DPA, the CKGS-DPA uses two power leakage points for a combined attack. The first power leakage point is used to determine the key candidate interval, and the second is used for the final attack. First, we study the law of MAD values distribution when the attack point is AddRoundKey and explain why this point is not suitable for DPA. According to this law, we modify the selection function to change the distribution of MAD values. Then a key-related value screening algorithm is proposed to obtain key information. Finally, we construct two key candidate intervals of size 16 and reduce the key guessing space of the SubBytes attack from 256 to 32. Simulation experimental results show that CKGS-DPA reduces the power traces demand by 25% compared with DPA. Experiments performed on the ASCAD dataset show that CKGS-DPA reduces the power traces demand by at least 41% compared with DPA.
通讯机构：
[Xiaoman Liang] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China
关键词：
Face key point detection;Facial segmentation;Peking Opera;Style transfer
通讯机构：
[Yaqi Sun] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
通讯机构：
[Lang Li] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, 421002, China
作者机构：
[Chen, Wenhui] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Wu, Guanchen] Guizhou Commun Polytech, Dept Informat Engn, Guiyang 551400, Peoples R China.;[Jung, Hoekyung] Paichai Univ, Dept Comp Sci & Engn, 155-40 Baejae Ro, Daejeon 35345, South Korea.
通讯机构：
[Hoekyung Jung] D;Department of Computer Science and Engineering, Paichai University, 155-40 Baejae-ro, Daejeon 35345, Korea<&wdkj&>Author to whom correspondence should be addressed.
关键词：
deep learning;you only look once (YOLO);number of people detection;distributed probability-adjusted confidence (DPAC)
摘要：
Compared to traditional detection methods, image-based flow statistics that determine the number of people in a space are contactless, non-perceptual, and high-speed statistical methods that have broad application prospects and potential economic value in business, education, transportation, and other fields. In this paper, we propose that the distributed probability-adjusted confidence (DPAC) function can optimize the reliability of model prediction according to the actual situation. That is, the reliability can be adjusted using the distribution characteristics of the target in the field of view, and a target can be determined with a confidence level that is greater than 0.5 and more accurately. DPAC can assign different target occurrence probability weights to different regions according to target distribution. Adding the DPAC function to a YOLOv4 network model on the basis of having the target confidence of the YOLOv4 network can reduce or improve confidence according to the target distribution and can then output the final confidence level. Using YOLOv4 + DPAC on the brainwash dataset can improve precision by 0.05% compared to the YOLOv4 model when the target confidence threshold is equal to 0.5; it can improve the recall of the model by 0.12% and the AP of the model by 0.12%. This paper also proposes that the distribution in the DPAC function be obtained based on unsupervised learning and verifies its effectiveness.
作者机构：
[Wu, Guanchen] Dept Informat Engn, Guizhou Commun Polytech, Guiyang 551400, Peoples R China.;[Chen, Wenhui] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Jung, Hoekyung] Paichai Univ, Dept Comp Sci & Engn, 155-40 Baejae Ro, Daejeon 35345, Peoples R China.
通讯机构：
[Hoekyung Jung] D;Department of Computer Science and Engineering, Paichai University, 155-40 Baejae-ro, Daejeon 35345, Korea<&wdkj&>Author to whom correspondence should be addressed.
关键词：
deep learning;recurrent neural network;spatiotemporal prediction;precipitation nowcasting
摘要：
Precipitation nowcasting predicts the future rainfall intensity in local areas in a brief time that impacts directly on human life. In this paper, we express the precipitation nowcasting as a spatiotemporal sequence prediction problem. Predictive learning for a spatiotemporal sequence aims to construct a model of natural spatiotemporal processes to predict the future frames based on historical frames. The spatiotemporal process is an abstraction of some of the spatial things in nature that change with time, and they usually do not change very dramatically. To simplify the model and facilitate the training, we considered that the spatiotemporal process satisfies the generalized Markov properties. The natural spatiotemporal processes are nonlinear and non-stationary in many aspects. The processes are not satisfied with the first-order Markov properties when making predictions, such as the nonlinear movement, expansion, dissipation, and intensity enhancement of echoes. To describe such complex spatiotemporal variations, higher-order Markov models need to be used for the modeling. However, many of the previous models for spatiotemporal prediction constructed were based on first-order Markov properties, losing information on the higher-order variations. Thus, we propose a recurrent neural network which satisfies the multi-order Markov properties to create more accurate spatiotemporal predictions. In this network, the core component is the memory cell structure of the gated attention mechanism, which combines the current input information, extracts the historical state that best matches the existing input from the historical multi-period memory information, and then predicts the future. Through this principle of the gated attention, we could extract the historical state information that is richer and deeper to predict the future and more accurately describe the changing characteristics of motion. The experiments show that our GARNN network captures the spatiotemporal characteristics better and obtains excellent results in the precipitation forecasting with radar echoes.
通讯机构：
[Huihuang Zhao] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China
摘要：
Character style transfer is challenging, especially when working with Chinese characters. Compared with English characters, Chinese characters have a range of structures and font styles and have attracted a lot of attention in recent research. Some GAN-based methods were proposed for Chinese character style transfer; however, these methods were focused on a single character image ignoring Chinese sentences or multiple characters in one image. A Chinese poetry style transfer method is proposed to address the problem based on Chinese character style transfer. The proposed method includes Smooth L1 loss, which is used to generate superior images. A novel key-attention mechanism generative adversarial network (KAGAN) and a multi-channel discriminator are introduced to generate high-quality images of Chinese characters. The experiments demonstrate that our method is better than other transfer methods, and the proposed model has improved nearly 2% from the conventional methods according to the SSIM evaluation metric.
关键词：
Ciphers;Internet of Things;Encryption;Hardware;Wireless sensor networks;Software algorithms;Generators;Addition or AND;Rotation;XOR (ARX);generalized Feistel structure;Internet of Things (IoT) nodes;lightweight block cipher
摘要：
The advancement of the Internet of Things (IoT) has promoted the rapid development of low-power and multifunctional sensors. However, it is seriously significant to ensure the security of data transmission of these nodes. Meanwhile, sensor nodes have the characteristics of converting analog signals into digital signals for operation processing in wireless sensor networks (WSNs). Given the particularity of Addition or AND, Rotation, and XOR (ARX) operations, its round function can only be based on the Feistel structure or generalized Feistel structure, otherwise, the process of decryption cannot be completed correctly. Furthermore, the existing ARX ciphers have the problems of only changing half of the plaintext block in one round and iterating for many rounds. In this article, a new logical combination method of generalized Feistel structure and ARX operations is proposed to improve the diffusion speed of ARX ciphers, called Shadow. Shadow overcomes the shortcomings of traditional ARX ciphers that only diffuse half of the block in one round. To ensure the efficiency of the encryption hardware circuit while ensuring the security of the physical-layer signal, we studied the round-based hardware architecture and the serial hardware architecture for Shadow cipher. Particularly, we conducted a series of performance tests on Shadow, including the avalanche effect, FPGA implementation, and ASIC implementation. Also, we conducted a security analysis of the Shadow. As shown by our experiments and comparisons, Shadow is compact in IoT nodes and is of high security against cryptanalysis.
摘要：
Video-based face detection and tracking technology has been widely used in video surveillance, safe driving, and medical diagnosis. In video sequences, most existing face detection and tracking methods face interference caused by occlusion, ambient illumination, and changes in human posture. To accurately track human faces in video sequences, we propose an efficient face detection and tracking framework based on deep learning, which includes a SENResNet face detection model and a Regression Network-based Face Tracking (RNFT) model. Firstly, the SENResNet model integrates the Squeeze and Excitation Network (SEN) with the Residual Neural Network (ResNet). To solve the problem that deep neural networks are difficult to train, we use ResNet to overcome the problem of gradient disappearance in deep network training. To fuse the features of each channel during the convolution operation, we further integrate the SEN module into the SENResNet model. SENResNet accurately detects facial information in each frame and extracts the position of the target face, thereby providing an initialization window for face tracking. Then, the RNFT model extracts facial features from adjacent frames and predict the position of the target face in the next frame. To address the problem of feature scaling, we add a correction network to the RNFT model. The improved RNFT model extracts the rectangular frame of the target face in the previous frame and strengthens the perception of feature scaling, thereby improving its accuracy. Extensive experimental results on public facial and video datasets show that the proposed SENResNet and RNFT models are superior to the state-of-the-art comparison methods in terms of accuracy and performance. (c) 2021 Elsevier Inc. All rights reserved.
作者机构：
[Ramasamy, Manimaran; Zhao, Huihuang; Wang, Ying; Liu, Qingyun; Zhang, Feng] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Zhao, Huihuang; Wang, Ying; Liu, Qingyun] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.;[Qiao, Zhijun] Univ Texas Rio Grande Valley, Sch Math & Stat Sci, Brownsville, TX 78520 USA.
通讯机构：
[Huihuang Zhao] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang 421002, China
关键词：
Conditional GANs;Edge constraint;Laplace operator;Pix2Pix;Sketch to portrait
作者：
Zhang, Feng;Zhao, Huihuang;Ying, Wang;Liu, Qingyun;Raj, Alex Noel Joseph;...
期刊：
Intelligent Automation and Soft Computing,2020年26(6):1391-1401 ISSN：1079-8587
通讯作者：
Zhao, H.
作者机构：
[Zhao, Huihuang; Liu, Qingyun; Ying, Wang; Zhang, Feng] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Zhao, Huihuang; Liu, Qingyun; Ying, Wang] Hunan Prov Key Lab Intelligent Informat Proc & Ap, Hengyang 421002, Peoples R China.;[Raj, Alex Noel Joseph] Key Lab Digital Signal & Image Proc Guangdong, Shantou 515063, Peoples R China.;[Fu, Bin] Univ Texas Rio Grande Valley, Dept Comp Sci, Edinburg, TX USA.
通讯机构：
[Zhao, H.] C;[Zhao, H.] H;College of Computer Science and Technology, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and ApplicationChina
关键词：
Edge optimization;Generative adversarial networks;Human face sketch;RGB image
摘要：
The cryptographic algorithm has been gradually improved in design, but its implementations are vulnerable to side-channel analysis (SCA). Generally speaking, adding a mask to the primitive is the best way to counteract SCA. In the high-order mask, the key to affecting performance and security lies in the multiplication design. Based on the research of the advanced encryption standard (AES) algorithm, internal round function structure, and zero-knowledge proof, a high-order AES mask scheme is designed to optimise the implementation. In this scheme, the substitution-box protects sensitive variables in the algorithm with the use of secure multiplication and secure inversion by column. The scheme named as in columns higher-order mask (ICHM), features low cost and high security. The result of the experiment proves the security and effectiveness of the ICHM.
摘要：
As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user's power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.
期刊：
International Journal of Information Security,2020年19(3):303-310 ISSN：1615-5262
通讯作者：
Liu, Yining;Jiang, Chengshun
作者机构：
[Liu, Yining; Zhou, Yuanjian] Henyang Normal Univ, Coll Comp Sci, Technology, Henyang, Peoples R China.;[Liu, Yining] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.;[Jiang, Chengshun] Yangtze Normal Univ, Coll Big Data, Intelligent Engn, Chongqing, Peoples R China.;[Wang, Shulan] Shenzhen Technol Univ, Coll Big Data, Internet, Shenzhen, Peoples R China.
通讯机构：
[Liu, Yining] H;[Liu, Yining] G;[Jiang, Chengshun] Y;Henyang Normal Univ, Coll Comp Sci, Technology, Henyang, Peoples R China.;Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin, Peoples R China.
关键词：
Blockchain;E-voting;Hyperledger and Blind signature
摘要：
The traditional FOO e-voting protocols adopt centralized and non-transparent count center, which leads to distrust to the center and doubts the fairness and correctness of the vote. However, blockchain is the most innovative technology in the current era and promises to solve the trust problem in the system with one center. In this paper, an improved FOO e-voting protocol is proposed using blockchain, which tries to address the limitation or weakness in existing systems. The traditional trusted third party is replaced by smart contract; specifically, our scheme is deployed using hyperledger fabric. The implementation is enforced by the consensus mechanism, which ensures the security of the blockchain. Through the analysis, the proposed scheme is proved to satisfy the necessary requirements for an e-voting protocol; meantime the trust assumption is reduced significantly. Therefore, the proposed protocol is more versatile and practical.
摘要：
This paper develops a novel adaptive gradient-based block compressive sensing (AGbBCS_SP) methodology for noisy image compression and reconstruction. The AGbBCS_SP approach splits an image into blocks by maximizing their sparsity, and reconstructs images by solving a convex optimization problem. In block compressive sensing, the commonly used square block shapes cannot always produce the best results. The main contribution of our paper is to provide an adaptive method for block shape selection, improving noisy image reconstruction performance. The proposed algorithm can adaptively achieve better results by using the sparsity of pixels to adaptively select block shape. Experimental results with different image sets demonstrate that our AGbBCS_SP method is able to achieve better performance, in terms of peak signal to noise ratio (PSNR) and computational cost, than several classical algorithms.
作者机构：
[Zhao, Hui-Huang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.;[Lai, Yu-Kun; Rosin, Paul L.] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales.;[Wang, Yao-Nan] Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China.
通讯机构：
[Zhao, Hui-Huang] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang, Peoples R China.
关键词：
Deep neural networks;Style transfer;Soft mask;Semantic segmentation
摘要：
This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the content image while having the style transferred. Each soft mask of the style image represents a specific part of the style image, corresponding to the soft mask of the content image with the same semantics. Both the soft masks and source images are provided as multichannel input to an augmented deep CNN framework for style transfer which incorporates a generative Markov random field model. The results on various images show that our method outperforms the most recent techniques.
