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Adversarial Thumbnail-Preserving Transformation for Facial images Based on GAN

IEEE SIGNAL procESSING LETTERS 2023年 30卷 1147-1151页

作者： Zhou, Wentao Zhang, Yushu Zhao, Ruoyu Yi, Shuang Lan, Rushi Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Southwest Univ Polit Sci & Law Criminal Invest Sch Chongqing 401120 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

People are used to capturing their daily lives with images and uploading them to the cloud for easy archiving. While cloud storage services bring convenience to users, malicious humans and artificial intelligence machines may pose privacy threats to these uploaded images. Thumbnail-preserving encryption (TPE) is a solution for handling privacy threats posed by the usage of cloud storage and it is able to balance the privacy against humans and usability for users. However, the present TPE schemes display certain vulnerabilities when subjected to machine-based attacks and when the size of the thumbnail block is small, the facial recognition (FR) models exhibit strong capability in recognizing the identity within the protected facial images. To this end, we propose an adversarial thumbnail-preserving transform scheme for facial images, which can not only balance privacy and usability but also resist recognition from FR models. The experiments demonstrate that the proposed scheme has a better ability to resist machine recognition compared with existing schemes.

关键词： Facial recognition GAN privacy protection usability

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A sentiment analysis method for COVID-19 network comments integrated with semantic concept

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Li, Jun Jiang, Lanlan Huang, Guimin Zhang, Jingwei Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China

In recent years, the new coronavirus COVID-19 has brought great disaster and loss to the world and is still spreading around the world. The situation in China is generally well controlled, and the lockdown has been removed, but the comments and messages about the epidemic persist online. For people working and living normally in China, their attitudes and views toward COVID-19 directly reflect the current situation of the pandemic. This paper collected Chinese microblogs, forums, and online comments, identified the latest comments about COVID-19, and conducted a sentiment analysis of them. Specifically, we proposed a new sentiment analysis method that integrated the semantics of words with the text analyzed. Different from the traditional sentiment analysis method which only relied on sentiment words, the proposed method extended the semantic concepts of affective words by integrating the semantic conceptual information about the affective words from the context of the comments and thus, provided information to support the final judgment of the affective opinions. The proposed approach incorporated the part-of-speech embedding information along with word embedding and relies on semantic concepts to enhance the emotional expression of words in context. The experimental results showed that by integrating the semantics of words, the accuracy of sentiment analysis is substantially improved, and it also reflected that different semantics of the same word have different influences on sentiment analysis. On several benchmark datasets, there was a 3-6% improvement in accuracy.

关键词： Sentiment analysis Semantic concept Fuse models Covid-19

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RNE-DSNet: A Re-parameterization Neighborhood Enhancement-based Dual-Stream Network for CT image recognition

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2024年 56卷

作者： Zhou, Tao Liu, Fengzhen Ye, Xinyu Guo, Yujie Niu, Yuxia Lu, Huiling North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China Ningxia Med Univ Sch Med Informat & Engn Yinchuan 750004 Peoples R China North Minzu Univ Key Lab Image & Graph Intelligent Proc State Ethn Affairs Commiss Yinchuan 750021 Peoples R China

Deep learning models based on Transformer and CNN are current research hotspots. However, there are more time complexity and space complexity in extracting global-local features, the feature extraction ability is insufficient for small lesions in CT images. A Re-parameterization Neighborhood Enhancement-based DualStream Network (RNE-DSNet) is proposed for CT image recognition in this paper. This method improves the ability to recognize small lesions while reducing the Params number and FLOPs, which gives a trade-off between performance and cost. The main works are as following: Firstly, a Re-parameterization Neighborhood Enhancement-based Dual-Stream Network is designed to improve the global-local feature extraction ability. Secondly, the Re-parameterization Group Residual Block is designed in the CNN branch, which improves the local feature extraction ability through multi-scale and multi-branch structures, and the number of the Params and FLOPs are reduced effectively. The Neighborhood Enhancement Transformer Block is designed in the Transformer branch, in which the global attention is calculated by parallel Hadamard product and Dot product, and the global attention maps are enhanced by deep convolution operation to embed local features in global features. Thirdly, the Adaptive-random Mask Cropping method is designed to improve the features extraction ability for small lesions in CT images through heat map and random Mask techniques. RNE-DSNet is compared with other SOTA models. The Accuracy (ACC), Precision (PRE), Recall (REC), F1 score (F1), and Area Under Curve (AUC) values of RNE-DSNet are reached 99.22%, 99.23%, 99.08%, 99.23% and 99.38%, respectively, which verified the effectiveness of the RNE-DSNet model. The number Params and FLOPs are reduced by 62.78% and 84.20%, respectively. The effectiveness of RNE-DSNet is verified by heat map visualization. The performance of RNE-DSNet is generally better than the others methods. There is positive significance for

关键词： CT images Dual -stream network Re -parameterization group residual block Neighborhood enhancement transformer Adaptive -random mask cropping

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Visually Semantics-Aware Color image Encryption Based on Cross-Plane Substitution and Permutation

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第8期20卷 10576-10586页

作者： Zhang, Yongming Zhao, Ruoyu Zhang, Yushu Yi, Shuang Lan, Rushi Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Nanjing Hangda Cyberspace secur Co Ltd Nanjing 211100 Peoples R China Southwest Univ Polit Sci & Law Chongqing Educ Comm Coll Criminal Invest Engn Res Ctr Forens Sci Chongqing 401120 Peoples R China

The concept of thumbnail-preserving encryption (TPE) is proposed to strike a balance between image privacy and visual usability. It allows users to have both without having to choose between privacy and usability, but it processes the information within a channel and not between channels. Recently, two cross-plane TPE schemes have been proposed in an attempt to process the information between channels. However, in a sense, they are not completely cross-plane. In addition, as with other TPE schemes, they do not respect the syntax of the color image. They simply treat each channel as a regular numerical matrix for the substitution encryption, and thus there are issues of out of bounds and some elements being left unencrypted. In this article, we propose a TPE scheme for cross-plane substitution and permutation. As with other TPE schemes, a certain amount of visually semantics-aware information is preserved in the encrypted image. Meanwhile, this TPE scheme is the first to implement a color image compliant syntax. In general, a color image composes of three channels, meaning a (pixel) point has three elements. In the proposed scheme, substitution encryption can directly be used for a point (three elements across planes). It eliminates possibly going out of bounds and ensures that all elements in the image are encrypted during substitution.

关键词： Color image encryption lightweight cryptography privacy protection usable security and privacy

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Dual-stream pyramid and attention network for stereo matching

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JOURNAL OF intelligent & FUZZY SYSTEMS 2024年第2期46卷 4909-4922页

作者： Shi, Liqing Xiong, Taiping Cui, Gengshen Pan, Minghua Zhu, Zhiguo Cheng, Wei Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin Peoples R China

In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://***/Shi-LiQing/DSPANet.

关键词： Stereo matching dual-stream pyramid attention mechanism binocular vision

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Difference Value Network for image Super-Resolution

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IEEE SIGNAL procESSING LETTERS 2021年 28卷 1070-1074页

作者： Jiang, Zetao Pi, Kui Huang, Yongsong Qian, Yi Zhang, Shaoqin Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Nanchang Hangkong Univ Nanchang 330063 Jiangxi Peoples R China

Recently, improved performance has been achieved in image super-resolution (SR) by using deep convolutional neural networks (CNNs). However, most existing networks neglect the feature correlations of adjacent layers, causing features at different levels to not be fully utilized. In this paper, a novel difference value network (DVN) is proposed to address this problem. The proposed network makes full use of different levels of features by using the difference values (D-values) of adjacent layers. Specifically, a difference value block (DVB) is designed to extract the difference values of adjacent layers. The extracted difference value can highlight which regions should be paid more attention to, so as to guide image SR. Further, a difference value group (DVG) is designed to integrate the difference values extracted by the difference value block into its output. In this way, the DVG can provide additional structure prior for image SR. Finally, to make our network more stable, a multipath supervised reconstruction block is proposed to supervise the reconstruction process. The experimental results on five benchmark datasets show that the proposed network can achieve better reconstruction results than the compared SR methods.

关键词： image reconstruction Feature extraction Convolution Superresolution Training graphics Correlation image super-resolution deep convolutional neural networks adjacent layers difference value

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GAN review: Models and medical image fusion applications

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INFORMATION FUSION 2023年 91卷 134-148页

作者： Zhou, Tao Li, Qi Lu, Huiling Cheng, Qianru Zhang, Xiangxiang North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China North Minzu Univ Key Lab Image & Graph Intelligent Proc State Ethn Affairs Commiss Yinchuan 750021 Peoples R China Ningxia Med Univ Sch Sci Yinchuan 750004 Peoples R China

Generative Adversarial Network (GAN) is a research hotspot in deep generative models, which has been widely used in the field of medical image fusion. This paper summarizes GAN models from the following four aspects: firstly, the basic principles of GAN are expounded from two aspects: basic model and training process;secondly, variant GAN models are summarized into three directions (Probability Distribution Distance, Overall Network Architecture, Neural Network Structure), from the methods based on f-divergence, the methods based on IPM, Single-Generator and Dual-Discriminators GAN, Multi-Generators and Single-Discriminator GAN, Multi -Generators and Multi-Discriminators GAN, Conditional Constraint GAN, Convolutional Neural Network struc-ture GAN and Auto-Encoder Neural Network structure GAN are eight dimensions to summarize the typical models in recent years;thirdly, the advantages and application of GAN models in the field of medical image fusion are explored from three aspects;fourthly, the main challenges faced by GAN and the challenges faced by GAN models in medical image fusion field are discussed and the future prospects are given. This paper sys-tematically summarizes various models of GAN, advantages and challenges of GAN models in medical image fusion field, which is very important for the future research of GAN.

关键词： Generative adversarial network (GAN) Generative models Medical image fusion Probability distribution distance Multi -generators and multi -discriminators GAN

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label Guided Discrete Hashing for Cross-Modal Retrieval

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION SYSTEMS 2022年第12期23卷 25236-25248页

作者： Lan, Rushi Tan, Yu Wang, Xiaoqin Liu, Zhenbing Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Nanning Res Inst Guilin 541004 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Due to their low storage capacity and fast retrieval speed, hashing techniques have received much attention in crossmodal retrieval. However, there are some issues that need to be further explored. First, some existing hashing methods use the labels to construct the semantic similarity matrix between pairwise data, ignoring the potential manifold structure between heterogeneous data. Second, some existing methods underestimate the importance of multi-label and the gaps between different class labels, making the learned hash codes less discriminative. Third, few of them embed both manifold and balanced structures within the same model, and the relaxation of discrete constraints will lead to an increasing quantization error. To mitigate these problems, this paper proposes a novel supervised hashing method, termed label Guided Discrete Hashing (LGDH), which simultaneously preserves the comprehensive manifold structure and discriminative balanced codes that are both constructed by label information into Hamming space. We develop a local category distribution of the nearest neighbors, to excavate the underlying manifold structure of heterogeneous data. To maximize the gaps of different categories, a balanced matrix is constructed by labels to generate hash codes with balanced bits. For multi-label data, we also design a novel multi-label manifold and balanced structure matrix to adapt the real-world scenarios. An effective discrete optimization method is used to optimize our proposed objective function instead of the relaxation one. Extensive experiments on three benchmark datasets verify the effectiveness of LGDH. The comparison results demonstrat that LGDH achieves about 2% and 3% improved to different cross-modal tasks on average.

关键词： Cross-modal retrieval manifold embedding hash learning balanced matrix

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Event-triggered integral sliding mode control of two-time-scale systems

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NONLINEAR DYNAMICS 2025年第2期113卷 1263-1277页

作者： Yu, Tian-Tian Wang, Yan-Wu Yang, Wu Lei, Yan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China GuangXi Univ Sch Elect Engn Nanning 530004 Peoples R China Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Gener Technol & Syst Serv Robot Chongqing 400715 Peoples R China

This paper investigates the H-infinity performance of two-time-scale systems with nonlinear uncertainty and external disturbance via the event-triggered integral sliding mode control (ETISMC) strategy. To facilitate the analysis of the sliding mode surface's reachability and the design of the event-triggered (ET) integral sliding mode controller, the epsilon-dependent integral sliding function with the event-triggered mechanism is constructed. Based on H-infinity control theory, the integral sliding mode controller gain and the ET parameter matrix are co-designed through the solution of a set of linear matrix inequalities. The proposed event-triggered mechanism can avoid Zeno behavior. Furthermore, an adaptive ETISMC strategy is proposed to address the unknown upper bounds of uncertainty and external disturbance. Finally, the effectiveness, applicability, and advantages of the control strategies are verified by simulations and comparative analysis.

关键词： Two-time-scale system H-infinity performance Event-triggered mechanism Integral sliding mode control Adaptive control

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Discriminative Face Hallucination via Locality-Constrained and Category Embedding Representation

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第12期51卷 7314-7325页

作者： Liu, Licheng Lan, Rushi Wang, Yaonan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

Recent years have witnessed the rapid development of face image hallucination techniques. However, the previous face hallucination methods are unsupervised and ignore the label information of training samples, leading to undesirable results. This article proposes a locality-constrained and category embedding representation (LCER) method to super-resolve face image in a supervised manner by embedding the label information in data representation. The proposed LCER incorporates the locality prior and category information into one unified framework, which aims to learn both the advantages of locality in preserving the true typologic structure of data manifold and the discriminability in exposing the class subspace information. Such strategy allows the LCER not only to preserve more sharpen image details but also to guarantee the face structure pattern be transferred mainly from the same subject in super-resolution reconstruction. Extensive experiments were conducted to evaluate the proposed LCER, and the comparative results demonstrate that it achieved superior face hallucination performance in both the quantitative measurements and visual impressions compared to several state-of-the-art.

关键词： Face Training image reconstruction Face recognition Manifolds Category embedding discriminative face hallucination locality-constrained representation (LcR) manifold learning

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