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Towards Black-Box Adversarial Attacks on Interpretable Deep Learning Systems

Towards Black-Box Adversarial Attacks on Interpretable Deep ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhan, Yike Zheng, Baolin Wang, Qian Mou, Ningping Guo, Binqing Li, Qi Shen, Chao Wang, Cong School of Cyber Science and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Hubei China Institute of Network Sciences and Cyberspace BNRist Tsinghua University Beijing China School of Cyber Science and Engineering Xi'an Jiaotong University Shaanxi China City University of Hong Kong Departrnent of Computer Science Hong Kong Hong Kong

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recent works have empirically shown that neural network interpretability is susceptible to malicious manipulations. However, existing attacks against Interpretable Deep Learning Systems (IDLSes) all focus on the white-box setting, which is obviously unpractical in real-world scenarios. In this paper, we make the first attempt to attack IDLSes in the decision-based black-box setting. We propose a new framework called Dual Black-box Adversarial Attack (DBAA) which can generate adversarial examples that are misclassified as the target class, yet have very similar interpretations to their benign cases. We conduct comprehensive experiments on different combinations of classifiers and interpreters to illustrate the effectiveness of DBAA. Empirical results show that in all the cases, DBAA achieves high attack success rates and Intersection over Union (IoU) scores. © 2022 IEEE.

关键词： Learning systems

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Is the Performance of NOMA-aided Integrated Sensing and Multicast-Unicast Communications Improved by IRS?

Is the Performance of NOMA-aided Integrated Sensing and Mult...

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IEEE International Conference on Communications Workshops, ICC

作者： Yang Gou Yinghui Ye Guangyue Lu Lu Lv Rose Qingyang Hu Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China School of 'Telecommunications Engnieering Xidian University Xi'an China Department of Electrical and Computer Engineering Utah State University U.S.A.

In this paper, we consider intelligent reflecting surface (IRS) in a non-orthogonal multiple access (NOMA)-aided Integrated Sensing and Multicast-Unicast Communication (ISMUC) system, where the multicast signal is used for sensing and communications while the unicast signal is used only for communications. Our goal is to depict whether the IRS improves the performance of NOMA-ISMUC system or not under the imperfect/perfect successive interference cancellation (SIC) scenario. Towards this end, we formulate a non-convex problem to maximize the unicast rate while ensuring the minimum target illumination power and multicast rate. To settle this problem, we employ the Dinkelbach method to transform this original problem into an equivalent one, which is then solved via alternating optimization algorithm and semidefinite relaxation (SDR) with Sequential Rank-One Constraint Relaxation (SROCR). Based on this, an iterative algorithm is devised to obtain a near-optimal solution. computer simulations verify the quick convergence of the devised iterative algorithm, and provide insightful results. Compared to NOMA-ISMUC without IRS, IRS-aided NOMA-ISMUC achieves a higher rate with perfect SIC but keeps the almost same rate in the case of imperfect SIC.

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A permission generation and configuration method based on Rules and FP-Growth algorithm

A permission generation and configuration method based on Ru...

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2021 International Conference on networking and network Applications, NaNA 2021

作者： Zhu, Lei Zhang, Ziheng Hei, Xinhong Wang, Yichuan Yang, Ziliang Hu, Feixiong He, Ping Xi'an University of technology School of Computer Science and Engineering Shaanxi Xi'an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi'an710048 China Shenzhen Tencent Computer System Co. Ltd Shenzhen518054 China

ISBN: (纸本)9781665441582

With the development of computer technology, lots of enterprises had begun to build a data platform, and the data and its services already paly the import role in enterprises. However, the guarantee the data security is the primary task of platform, and data access control, especially the fine-grained access control model, had become an important means to enhance the security of platform. In this paper, we propose a data access permission configuration method based on rules and FP-growth. Specifically, FP-Growth algorithm is first used to obtain the frequent items and the association relations of data, which can be transformed into the enumerable permission configuration items. Then, the correspondence and frequency of data items are calculated to acquire the frequent items, the permission configuration acting on the data table columns is obtained according to the frequency of used data items. By filtering the strong association relation, the data items that are more closely related in the association relation and the corresponding data item values are finally obtained, and they are converted into the permission configuration that acts on the rows of the data table. The proposed method has been tested and verified to meet business needs, and the performance consumption is below the threshold. Moreover, it is feasible to utilize classical data mining algorithms to generate permission configuration, which has begun to apply the Blueking Data Platform. © 2021 IEEE.

关键词： Data mining

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Time Slot Correlation-Based Caching Strategy for Information-Centric Satellite networks 1

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11th EAI International Conference on MOBILe Wireless MiddleWARE, Operating Systems and Applications, MOBILWARE 2022

作者： Xu, Rui Di, Xiaoqiang Chen, Jing Cao, Jinhui Luo, Hao Wang, Haowei Qi, Hui He, Xiongwen Lei, Wenping School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China School of Information Engineering Henan Institute of Science and Technology Xinxiang453003 China Jilin Province Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun130022 China Information Center Changchun University of Science and Technology 130022 Changchun China Beijing Institute of Spacecraft System Engineering Beijing100094 China Beijing Institute of Space Mechanic and Electricity Beijing100094 China

ISBN: (数字)9783031344978

ISBN: (纸本)9783031344961

With the successful manufacture of highly stable and high-performance satellite processors, caching on satellites has become possible. Information Centric networking (ICN) is introduced to satellite networks to address the growing need for diverse data services. However, existing caching policies in ICN still suffer from the shortage of rational planning of cache locations and redundancy of cache contents. Consequently, the time slot correlation-based caching strategy for satellite networks (TSCCS) is proposed. Firstly, the index of time slot correlation is designed to quantify the neighboring time slots’ coupling relationship, and then the DSAM network model with time slot correlation is established. Secondly, the significance of satellite nodes is evaluated from the local and global perspectives respectively based on the eigenvector centrality algorithm to filter out the dynamic set of cache nodes. Finally, probabilistic caching is performed at the set of caching nodes with the goal of reducing cached content redundancy, considering content popularity and access delay. The simulated outcomes indicate that the TSCCS caching policy is superior to the comparison method in the areas of cache hit rate, request latency, and server load. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Satellites

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CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation

CSLP: Collaborative Solution to Long-Tail Problem and Popula...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yan Huang Zhenyu Yang Wenyue Hu Baojie Xu Zhibo Zhang The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP.

关键词： Hands Heavily-tailed distribution Accuracy Collaboration Tail Linear programming Generators User experience IP networks Recommender systems

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Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

Comprehensive Feature Processing Based on Attention Mechanis...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Mao Yuan Zengbin Zhang Zhengyang Zhang Zhenhui Ding Guangxiao Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts comprehensive cosalient features while reducing background noise, and FEM enhances feature representation and adjusts attention weights for full object coverage. Additionally, we introduce an adversarial learning module (ALM) to improve prediction quality by reducing noise in the co-salient regions. Extensive experiments on three benchmark datasets—CoCA, CoSOD3k, and CoSal2015—demonstrate that our model significantly outperforms state-of-the-art methods. The source code is available at https://***/yaobaimiao/CFPAM.

关键词： Attention mechanisms Source coding Noise Object detection Benchmark testing Feature extraction Adversarial machine learning Finite element analysis Background noise Speech processing

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Disentangling Interest and Conformity Representation to Mitigate Popularity Bias for Sequential Recommendation

Disentangling Interest and Conformity Representation to Miti...

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International Joint Conference on Neural networks (IJCNN)

作者： Wenyue Hu Zhenyu Yang Yan Huang Zhibo Zhang Baojie Xu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The objective of sequential recommendation is to predict user preferences for items based on historical interaction sequences. This process often leads to a phenomenon known as popularity bias, where popular items are excessively recommended. Conformity, the tendency of users to follow popular items, is a significant factor contributing to this issue. Previous methods have not adequately disentangled conformity and interest, failing to accurately model users’ true intent. To address this, we propose a novel Disentangled Interest and Conformity Sequential Recommendation method (DICSRec) to mitigate the popularity bias. Specifically, we first design an Intent Encoding Module (IEM), which includes two independent encoders for conformity and interest to model their representations. To better disentangle these two factors, we design a disentangling task with proxy-based self-supervised learning and orthogonal regularization. Furthermore, to provide the Intent Encoding Module with more global information, we design a Global Conformity-aware Module (GCM), which supplies item popularity information and aids in enhancing user conformity representation. Lastly, recognizing the varying significance of user conformity and interest, we propose an adaptive Fusion Prediction Module (FPM) that adaptively aggregates user conformity and interest representations for final prediction. Experiments on four real-world datasets consistently demonstrate the superiority of our method over advanced sequential recommendation models. Code implementation is available at: https://***/lyra0611/DICSRec.

关键词： Adaptation models Codes Fuses Aggregates Neural networks Self-supervised learning Multitasking

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A Cross-Modal Interactive Memory network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xitong Ma Yuansen Kuang Lin Yuan Cheng Tian Yijie Zeng Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation; 2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily; 3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models.

关键词： Knowledge engineering Visualization Terminology Semantics MIMICs Memory management Radiology Feature extraction Data models Biomedical imaging

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yuansen Kuang Xitong Ma Jing Zhao Guangchen Wang Yijie Zeng Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices; previous models suffer from local detail loss of positional embedding in 3D images; previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models.

关键词： Solid modeling Image segmentation Adaptation models Three-dimensional displays Correlation Image coding Self-supervised learning Generators Biomedical imaging Tumors

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ERC DMSP: Emotion Recognition in Conversation Based on Dynamic Modeling of Speaker Personalities

ERC DMSP: Emotion Recognition in Conversation Based on Dynam...

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International Joint Conference on Neural networks (IJCNN)

作者： Xiaoyang Li Zhenyu Yang Zhijun Li Yiwen Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Emotion Recognition in Conversations (ERC) is a widely applicable task, whether in emotional chatbots or recommender systems for dialog scenarios. The ability of systems to accurately identify human emotions is a crucial component in ERC success. In everyday conversations, a speaker’s emotional expression is closely tied to their individual characteristics and personality traits. Furthermore, individuals exhibit significant variations in their expressions of the same emotion. Traditional methods for ERC often overlook these personality differences and dynamic changes, leading to unreliable results. To address this issue, we propose a conversation emotion recognition model based on dynamic modeling of speaker’s personality (ERC DMSP). In our approach, to extract a speaker’s personality profile, we design a personality capture module to model the personalities of individuals based on their historical utterances. Moreover, a bidirectional influence exists between a speaker’s personality tendencies and their linguistic behavior. The speaker’s personality profile is thus continually updated based on the ongoing dialog. Additionally, since the personalities of other speakers in the conversation can also affect the form and intensity of a speaker’s emotional expression, we construct a conversation emotion representation utilizing group personality traits. We then comprehensively incorporate the speaker’s personality into the emotion representation of each utterance at both the group and individual levels through the personality engagement module. Throughout this process, the representation of dialog and the speaker’s personality undergo hierarchical updates, leading to more accurate conversation emotion recognition. We conducted extensive experiments on four commonly used public benchmark datasets for ERC to evaluate the effectiveness of our proposed ERC DMSP model. The results demonstrate its efficacy, as it exhibits a significant improvement over other methods in popular

关键词： Measurement Emotion recognition Accuracy Codes Neural networks Oral communication Color

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