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Usability Enhanced Thumbnail-Preserving Encryption Based on Data Hiding for JPEG images

IEEE SIGNAL procESSING LETTERS 2023年 30卷 793-797页

作者： Ye, Xi Zhang, Yushu Xiao, Xiangli 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 & Grap Intelligent Proc Guilin 541004 Peoples R China

As an increasing number of people tend to upload personal images to cloud platforms such as iCloud, Google Drive, and Baidu Cloud, the issue of image privacy protection in clouds has attracted widespread attention. Thumbnail-preserving encryption (TPE) is a newly proposed technology especially for balancing usability and privacy of images stored in clouds. The TPE-encrypted image does not reveal anything other than the thumbnail and size of the original one. Although many well-designed TPE schemes have been put forward, they almost all operate in the spatial domain and are not compatible with JPEG images. The only two schemes that can be applied in the frequency domain were proposed by Wright et al. and Marohn et al., but both of them have disadvantages and do not consider non-visual usability. In this letter, we propose a usability enhanced TPE scheme especially for JPEG images by utilizing reversible data hiding and image encryption in the frequency domain. We first transform the original image into the frequency domain and select some specific locations for extra information recording in every block. The original coefficient values at the selected locations are then collected and embedded into the rest of the block before encryption. Finally, the extra information is expected to be recorded into the selected locations of every block for non-visual usability. Experiments confirm that the proposed scheme achieves the target of balancing enhanced usability and privacy.

关键词： Reversible data hiding thumbnail-preserving encryption visual privacy protection

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Infrared image Super-Resolution via Transfer Learning and PSRGAN

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IEEE SIGNAL procESSING LETTERS 2021年 28卷 982-986页

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

Recent advances in single image super-resolution (SISR) demonstrate the power of deep learning for achieving better performance. Because it is costly to recollect the training data and retrain the model for infrared (IR) image super-resolution, the availability of only a few samples for restoring IR images presents an important challenge in the field of SISR. To solve this problem, we first propose the progressive super-resolution generative adversarial network (PSRGAN) that includes the main path and branch path. The depthwise residual block (DWRB) is used to represent the features of the IR image in the main path. Then, the novel shallow lightweight distillation residual block (SLDRB) is used to extract the features of the readily available visible image in the other path. Furthermore, inspired by transfer learning, we propose the multistage transfer learning strategy for bridging the gap between different high-dimensional feature spaces that can improve the PSGAN performance. Finally, quantitative and qualitative evaluations of two public datasets show that PSRGAN can achieve better results compared to the SR methods.

关键词： Feature extraction Transfer learning Superresolution Training Generators Task analysis Neural networks Super-resolution infrared image transfer learning knowledge distillation generative dversarial networks image processing

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image denoising via deep residual convolutional neural networks

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SIGNAL image AND VIDEO procESSING 2021年第1期15卷 1-8页

作者： Lan, Rushi Zou, Haizhang Pang, Cheng Zhong, Yanru Liu, Zhenbing Luo, Xiaonan Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guilin Univ Elect Technol Natl & Local Joint Engn Res Ctr Satellite Nav & L Guilin 541004 Peoples R China

Recently, convolutional neural network (CNN)-based methods have achieved impressive performance on image denoising. Notably, CNN with deeper and thinner structures is more flexible to extract the image details. However, direct stacking some existing networks is difficult to achieve satisfactory denoising performance. In this paper, we propose a novel deep residual convolutional neural network (DRCNN) for image denoising. The main structure of DRCNN is the residual block that consists of two convolutional layers, and there are skip connections between these two convolutional layers without the batch normalization operation. The skip connection not only directly transfers the input image information to the hidden layer but also reduces the path length of gradient transfer, making the gradient transfer in a short path and alleviating the vanishing-gradient problem. DRCNN is compared with several state-of-the-art algorithms, and the experimental results demonstrated its denoising effectiveness.

关键词： image denoising Convolutional neural network Residual learning

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Application of intelligent Traffic Scene Recognition Based on Computer Vision 15th

Application of Intelligent Traffic Scene Recognition Based o...

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15th China Conference on Wireless Sensor Networks (CWSN)

作者： Lei, XiaoChun Li, Ren Lin, Kaihao Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Guangxi Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Guangxi Peoples R China

ISBN: (纸本)9789811681745;9789811681738

The acceleration of urbanization has led to increasingly prominent traffic problems. In the context of the intelligent era, road traffic management is in urgent need of transformation and upgrading. The development of intelligent transportation is an important task for the construction of a modern city. The intelligent transportation system has the ability to automatically sense and analyze road conditions, which provides great convenience for traffic operation and management. Based on computer vision and deep learning technology, this paper designs and implements an intelligent traffic scene recognition system. The system processes the images taken by the traffic surveillance camera, and mainly realizes the intelligent recognition of two complex traffic scenes. One is the traffic statistics of passing vehicles at the intersection, and the other is vehicle speeding detection. The main process of system realization is divided into five steps. First, the YOLOv4 target detection algorithm is used to detect the vehicle. Second, use the SORT algorithm to track vehicles in real time, and then use the vector product-based virtual line counting method to achieve traffic flow statistics. Third, adopt the HyperLPR Chinese license plate recognition framework based on deep learning to recognize a wide range of license plates with high accuracy. Fourth, a two-line speed measurement method based on computer vision is designed to realize vehicle speeding detection. Finally, PyQT5 and WEB technology are used to realize the visualization of traffic recognition data.

关键词： intelligent traffic system Traffic statistics Overspeed violation detection License plate recognition

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IRCM-Caps: An X-ray image detection method for COVID-19

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CLINICAL RESPIRATORY JOURNAL 2023年第5期17卷 364-373页

作者： Qiu, Shuo Ma, Jinlin Ma, Ziping North Minzu Univ Sch Comp Sci & Engn Yinchuan Peoples R China State Ethn Affairs Commiss Key Lab Intelligent Informat Proc Image & Graph Yinchuan Peoples R China North Minzu Univ Sch Math & Informat Sci Yinchuan Peoples R China North Minzu Univ Sch Comp Sci & Engn Yinchuan 750021 Peoples R China

ObjectiveCOVID-19 is ravaging the world, but traditional reverse transcription-polymerase reaction (RT-PCR) tests are time-consuming and have a high false-negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID-19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet. MethodsThe proposed model integrates CNN and CapsNet. And attention mechanism module and multi-branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal. ResultThe test dataset includes 1200 X-ray images (400 COVID-19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception-Resnet-v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively. ConclusionThe proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID-19 X-ray image dataset.

关键词： CapseNet cascade network CNN COVID-19 deep learning X-ray

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A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English Texts

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INFORMATION 2024年第11期15卷 708页

作者： Qiu, Zan Huang, Guimin Qin, Xingguo Wang, Yabing Wang, Jiahao Zhou, Ya Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Aerosp Technol Sch Comp Sci & Engn Guilin 541004 Peoples R China

The accuracy of traditional topic models may be compromised due to the sparsity of co-occurring vocabulary in the corpus, whereas conventional word embedding models tend to excessively prioritize contextual semantic information and inadequately capture domain-specific features in the text. This paper proposes a hybrid semantic representation method that combines a topic model that integrates conceptual knowledge with a weighted word embedding model. Specifically, we construct a topic model incorporating the Probase concept knowledge base to perform topic clustering and obtain topic semantic representation. Additionally, we design a weighted word embedding model to enhance the contextual semantic information representation of the text. The feature-based information fusion model is employed to integrate the two textual representations and generate a hybrid semantic representation. The hybrid semantic representation model proposed in this study was evaluated based on various English composition test sets. The findings demonstrate that the model presented in this paper exhibits superior accuracy and practical value compared to existing text representation methods.

关键词： text representation conceptual knowledge word embeddings information fusion

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Transfer Learning for Semi-supervised Classification of Non-stationary Data Streams 27th

Transfer Learning for Semi-supervised Classification of Non-...

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27th International Conference on Neural Information processing

作者： Wen, Yimin Zhou, Qi Xue, Yun Feng, Chao Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin Peoples R China Henan City Univ Sch Municipal & Surveying Engn Yiyang Peoples R China

ISBN: (纸本)9783030638221;9783030638238

In the scenario of data stream classification, the occurrence of recurring concept drift and the scarcity of labeled data are very common, which make the semi-supervised classification of data streams quite challenging. To deal with these issues, a new classification algorithm for partially labeled streaming data with recurring concept drift is proposed. CAPLRD maintains a pool of concept-specific classifiers and utilizes historical classifiers to label unlabeled data, in which the unlabeled data are labeled by a weighted-majority vote strategy, and concept drifts are detected by automatically monitoring the threshold of classification accuracy on different data chunks. The experimental results illustrate that the transfer learning from historical concept-specific classifiers can improve labeling accuracy significantly, the detection of concept drifts and classification accuracy effectively.

关键词： Concept drift Semi-supervised learning Transfer learning

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nD-CS: A circularly shifting chaotic map generation method

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CHAOS SOLITONS & FRACTALS 2024年 181卷

作者： Wu, Zihua Zhang, Yinxing Bao, Han Lan, Rushi Hua, Zhongyun Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Changzhou Univ Sch Microelect & Control Engn Changzhou 213164 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

Due to more complex behavior and larger number of control parameters, high -dimensional chaotic map generation methods may provide more satisfactory performance in various practical applications compared to low -dimensional counterparts. Designing n -dimensional (nD) chaotic map generation methods for generating chaotic maps in arbitrary dimensions with desired dynamics is an interesting but challenging task. In this paper, we propose a universal framework called the nD circularly shifting chaotic map generation method (nD-CS), which utilizes existing one-dimensional chaotic maps as seed maps to generate nD chaotic maps with complex and robust behaviors. Theoretical analysis proves that when specific criteria are met by its control parameters, our nD-CS can exhibit complex and robust hyperchaotic behavior in the sense of Lyapunov exponent. To demonstrate the effectiveness of our nD-CS, we first employ it to generate three examples of three-dimensional hyperchaotic maps. The results indicate high-performance indicators of these new chaotic maps. Furthermore, we compare the nD chaotic maps generated by our nD-CS to those produced by existing methods. The results demonstrate that our chaotic maps have a better overall performance.

关键词： Chaotic map generation High-dimensional chaotic map Hyperchaotic behavior Lyapunov exponent

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Sentence classification based on the concept kernel attention mechanism

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EAI ENDORSED TRANSACTIONS ON SCAlabLE INFORMATION SYSTEMS 2022年第1期10卷

作者： Li, Hui Huang, Guimin Li, Yiqun Zhang, Xiaowei Wang, Yabing Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

Sentence classification is important for data mining and information security. Recently, researchers have paid increasing attention to applying conceptual knowledge to assist in sentence classification. Most existing approaches enhance classification by finding word-related concepts in external knowledge bases and incorporating them into sentence representations. However, this approach assumes that all concepts are equally important, which is not helpful for distinguishing the categories of the sentence. In addition, this approach may also introduce noisy concepts, resulting in lower classification performance. To measure the importance of the concepts for the text, we propose the Concept Kernel Attention Network (CKAN). It not only introduces concept information into the deep neural network but also contains two attention mechanisms to assign weights to concepts. The attention mechanisms are the text-to-concept attention mechanism (TCAM) and the entity-to-concept attention mechanism (ECAM). These attention mechanisms limit the importance of noisy concepts as well as contextually irrelevant concepts and assign more weights to concepts that are important for classification. Meanwhile, we combine the relevance of concepts and entities to encode multi-word concepts to reduce the impact of the inaccurate representation of multi-word concepts for classification. We tested our model on five public text classification datasets. Comparison experiments with strong baselines and ablation experiments demonstrate the effectiveness of CKAN.

关键词： Sentence classification text conceptualization concept knowledge base attention mechanism concept embeddings

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Natural Backdoor Attacks on Deep Neural Networks via Raindrops

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SECURITY AND COMMUNICATION NETWORKS 2022年第0期2022卷

作者： Zhao, Feng Zhou, Li Zhong, Qi Lan, Rushi Zhang, Leo Yu Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3216 Australia

Recently, deep learning has made significant inroads into the Internet of Things due to its great potential for processing big data. Backdoor attacks, which try to influence model prediction on specific inputs, have become a serious threat to deep neural network models. However, because the poisoned data used to plant a backdoor into the victim model typically follows a fixed specific pattern, most existing backdoor attacks can be readily prevented by common defense. In this paper, we leverage natural behavior and present a stealthy backdoor attack for image classification tasks: the raindrop backdoor attack (RDBA). We use raindrops as the backdoor trigger, and they are naturally merged with clean instances to synthesize poisoned data that are close to their natural counterparts in the rain. The raindrops dispersed over images are more diversified than the triggers in the literature, which are fixed, confined, and unpleasant patterns to the host content, making the triggers more stealthy. Extensive experiments on imageNet and GTSRB datasets demonstrate the fidelity, effectiveness, stealthiness, and sustainability of RDBA in attacking models with current popular defense mechanisms.

关键词： Deep neural networks

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