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TextDefense: Adversarial Text Detection based on Word Importance Score Dispersion

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Shen, Lujia Pu, Yuwen Zhang, Xuhong Ge, Chunpeng Yang, Xing Peng, Hao Wang, Wei Ji, Shouling Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310027 China Zhejiang University School of Software Technology Zhejiang Ningbo315048 China Software School Shandong Jinan250100 China National University of Defense Technology State Key Laboratory of Pulsed Power Laser Technology Anhui Hefei200037 China Zhejiang Normal University School of Computer Science and Technology Zhejiang Jinhua321004 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China

Natural language processing (NLP) models are widely used in various scenarios, yet they are vulnerable to adversarial attacks. Existing works aim to mitigate this vulnerability, but each work targets a specific attack category or has computational overhead limitations, making them vulnerable to adaptive attacks. In this paper, we exhaustively investigate the adversarial attack algorithms in NLP and discover that existing attack algorithms mainly disrupt the importance distribution of words in a text. A well-trained model can distinguish subtle importance distribution differences between clean and adversarial texts. Based on this intuition, we propose TextDefense, a new adversarial example detection framework that utilizes the target model's capability to defend against adversarial attacks, requiring no prior knowledge. Unlike previous approaches, TextDefense is attack-type agnostic and outperforms existing methods in experiments with different architectures, datasets, and attack methods. We also discover that the target model's generalizability is a leading factor influencing the performance of TextDefense. Finally, we provide insights into the adversarial attacks in NLP and the principles of our defense method by analyzing the properties of the target model and the adversarial example. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

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Joint Deep Matching Model of OCT Retinal Layer Segmentation

引用

computers, Materials & Continua 2020年第6期63卷 1485-1498页

作者： Mei Yang Yuanjie Zheng Weikuan Jia Yunlong He Tongtong Che Jinyu Cong School of Information Science and Engineering at Shandong Normal University Jinan250358China Key Lab of Intelligent Computing and Information Security in Universities of Shandong Shandong Provincial Key Laboratory for Novel Distributed Computer Software TechnologyInstitute of Biomedical SciencesShandong Normal UniversityJinan250358China INSA Lyon University of LyonCNRSInsermVilleurbanne69621 CedexFrance

Optical Coherence Tomography(OCT)is very important in medicine and provide useful diagnostic *** retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular *** the existing retinal layer segmentation approaches,learning or deep learning-based methods belong to the ***,most of these techniques rely on manual-marked layers and the performances are limited due to the image *** order to overcome this limitation,we build a framework based on gray value curve matching,which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from *** depth convolution network learns the column correspondence in the OCT image *** whole OCT image participates in the depth convolution neural network operation,compares the gray value of each column,and matches the gray value sequence of the transformation column and the next *** this algorithm,when a boundary point is manually specified,we can accurately segment the boundary between retinal *** experimental results obtained from a 54-subjects database of both normal healthy eyes and affected eyes demonstrate the superior performances of our approach.

关键词： OCT retinal segmentation deep learning 1D convolution

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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Research on Assistant Diagnostic Method of TCM Based on BERT 11

Research on Assistant Diagnostic Method of TCM Based on BERT

引用

11th International Conference on Information technology in Medicine and Education, ITME 2021

作者： Xu, Chuanjie Yuan, Feng Chen, Shouqiang Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China Shandong Management University School of Information Engineering Jinan China Ctr. of Hear of the Second Affil. Hospital of Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (纸本)9781665406796

Traditional Chinese medicine (TCM) auxiliary diagnosis is a systematic diagnosis platform that uses computer modeling technology to assist TCM doctors in recording diseases, providing on time diagnoses, writing prescriptions, performing tele-medicine, and supporting medical teaching. This study proposes a Bidirectional Encoder Representations from Transformers TCM auxiliary diagnosis model using 20,000 items of TCM records. These records were collected from the outpatient clinic of the Second Affiliated Hospital of shandong University of Traditional Chinese Medicine. Specifically, our model aims to predict final diagnosis while taking TCM symptoms as inputs;for example, when we input relief of chest tightness but persistent tiredness and sluggishness, the model provides a diagnosis of chest paralysis. An experiment was conducted on these real-world Chinese medical data. Results show that our model achieves state-of-the-art performance. Hence, our proposed model can effectively use the information from the four diagnostic procedures in the TCM text. © 2021 IEEE.

关键词： Diagnosis

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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

引用

IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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Fire Video Image Detection Based on a Convolutional Neural Network 2

Fire Video Image Detection Based on a Convolutional Neural N...

引用

2019 2nd International Conference on computer Information Science and Artificial Intelligence, CISAI 2019

作者： Wang, Wen Li, Tianping Shandong Key Laboratory for Normal Distributed Computer Software Technology School of Physics and Electronic Science Shandong Normal University Jinan Shandong250358 China School of Physics and Electronic Science Shandong Normal University Jinan Shandong250358 China

computer vision-based and real-Time flame detection is very important to inmodern surveillance system. At present, convolutional neural network (CNN) has become a topic discussed by more and more researchers because of its high recognition accuracy and wide application. The preprocessind process of the traditional image processing method is complicated and the false positive rate is high. So, in this paper we proposed an algorithm for detecting flame in real time using CNN technology. Firstly, to improve the accuracy of detection, we proposed a suspicious target regions segmentation for disposing the suspected flame regions. This algorithm could locate the target area and segment the target area to improve the flame detection and recognition accuracy. Then, we designed a model based on CNN to classify the extracted feature maps of candidate areas. Finally, we could get the detection of flame according to the the classification results. The experimental results show that the approach has high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Gt-Simnet: Improving Code Automatic Summarization Via Multi-Modal Similarity Networks

SSRN

引用

SSRN 2021年

作者： Gao, Xuejian Jiang, Xue Wu, Qiong Wang, Xiao Lyu, Chen Lyu, Lei Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan250014 China

Code summarization aims to generate high-quality functional summaries of code snippets to improve the efficiency of program development and maintenance. It is a pressing challenge for code summarization models to capture more comprehensive code knowledge by integrating the feature correlations between the semantics and syntax of the code. In this paper, we propose a multi-modal similarity network based code summarization method: GT-SimNet. It proposes a novel code semantic modelling method based on a local API dependency graph (local-ADG), which exhibits an excellent ability to mask irrelevant semantics outside the current code snippet. For code feature fusion, GT-SimNet uses the SimNet network to calculate the correlation coefficients between local-ADG and AST nodes and perform fusion under the influence of the correlation coefficients. Finally, it completes the prediction of the target summary by the code generator. We conduct extensive experiments to evaluate the performance of GT-SimNet on two Java datasets. The results shows that GT-SimNet achieved BLEU scores of 38.73% and 39.22% on the two datasets, respectively, 1.58%~2.23% higher than the best existing baseline. Importantly, GT-SimNet reduces the BLEU scores by 20% after removing local-ADG. This indicates that local-ADG is effective for the semantic representation of the code. © 2021, The Authors. All rights reserved.

关键词： Semantics

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Reflections and Countermeasures on the Development of Secondary Vocational Teachers' Teaching Ability Based on AI and Students' Characteristics

Reflections and Countermeasures on the Development of Second...

引用

2020 International Conference on Artificial Intelligence and Education, ICAIE 2020

作者： Liu, Lining Zhang, Yongsheng Zhang, Penglong Sun, Wenqian Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9781728166599

The arrival of the era of artificial intelligence has a great impact on the produce of relationship and mode, resulting in changes in the demand for talents. As an important place to train professional and technical talents, secondary vocational schools need teachers to make changes. Teachers should improve their own educational and teaching ability, and cultivate talents of high-quality, innovative and in line with the needs of social development. By analyzing the development status of artificial intelligence in the field of education and the current students' problems facing teachers (students' characteristics), this paper gives some ideals and suggestions on the development of professional ability of secondary vocational teachers in the era of artificial intelligence. © 2020 IEEE.

关键词： Students

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Adaptive Intervention for Crowd Negative Emotional Contagion

Adaptive Intervention for Crowd Negative Emotional Contagion

引用

International Conference on computer Supported Cooperative Work in Design

作者： Yepeng Shi Guijuan Zhang Dianjie Lu Lei Lv Hong Liu Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9781728165981

Negative emotion will spread among the crowd, and make people irrational. Intervention can reduce crowd panic by isolating the source of transmission or spreading positive emotions, which is an effective measure to ensure the safety of the crowd. However, it is still a very challenging problem that how to use appropriate intervention to maximize the influence to infected individuals while minimizing the cost of the intervention. To this end, we present an intervention optimization method for crowd emotional contagion (IO-CEC) to adaptively intervene in negative emotional contagion. First, we build a stochastic events based emotional contagion model (SEEC) to capture the dynamic evolution characteristics of crowd emotional contagion. In this model, we combine the SIS infection model and the Markov stochastic process to study the dynamic change of the number of infected individuals. Second, we propose a Hawkes process based intervention model (HIM) to describe the intensity of intervention. By using the Hawkes stochastic point process, two intervention intensity functions are defined to quantify the infection rate and recovery rate. Then, in order to improve the efficiency of intervention, we formulate the intervention optimization as a problem of maximizing the utility and use a heuristic algorithm to solve it. Finally, we implement a crowd emotional contagion simulation system to visualize the results of emotional intervention. Experimental results show that our method is effective and has great significance for guiding realistic emotional intervention.

关键词： Adaptation models Visualization Computational modeling Heuristic algorithms Stochastic processes Optimization methods Manuals

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Multiple Feature Fusion-based Video Face Tracking for IoT Big Data

arXiv

引用

arXiv 2021年

作者： Li, Tianping Liu, Zhifeng Qiao, Jianping Key Laboratory of Medical Physics and Image Processing in Shandong Province Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University School of Physics and Electronics Jinan Shandong China School of Physics and Electronics Shandong Normal University JinanShandong China School of Physics and Electronics Shandong Normal University Jinan China

With the advancement of IoT and artificial intelligence technologies, and the need for rapid application growth in fields such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. In this paper, we propose a multi-feature fusion algorithm based on integral histograms and a real-time update tracking particle filtering module. First, edge and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the particle filtering algorithm to simplify the calculation steps of complex particles. Finally, the tracking window size is adjusted in real time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti-interference ability and robustness. © 2021, CC BY.

关键词： Monte Carlo methods

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