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Advancing MobileNet security: Weighted Adversarial Learning in Convolutional Neural networks

Advancing MobileNet Security: Weighted Adversarial Learning ...

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Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Hakeem Quadri Bruce Gu Hua Wang Sardar Islam Department of Information Technology Victoria University Melbourne Australia Key Laboratory of Computing Power Network and Information Security Qilu University of Technology Jinan China Institute for sustainable Industries and Liveable Cities Victoria University Melbourne Australia

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

Convolutional Neural networks (CNN), particularly low latency models such as MobileNets have excelled in many applications, including object detection in images, speech recognition and natural language processing, but they are vulnerable to subtle perturbations that are virtually imperceptible to the human eye yet can deceive the network into misclassifying images. To enhance the robustness of such CNNs against adversaries, conventional adversarial training methods treat all data points as equally important and susceptible to attack. A weighted adversarial learning algorithm is developed in a Stackelberg game framework. This approach prioritizes data points that are more susceptible to attacks during network training. To further optimize the algorithm, we employ a Reinforcement Learning ($\mathbf{R L}$) agent to fine-tune the hyperparameters of the model, thereby increasing its robustness. Our findings indicate an increased robustness of $66.18 \%$ of the Weighted Adversary Reinforced Stackelberg Learning (WARS) against the traditional adversarial training of $64.72 \%$ in a one epoch training, using the CIFAR-10 dataset. We conclude that the WARS represents a valuable adversarial training method for bolstering the robustness of low latency CNN models.

关键词： Training Perturbation methods Games Speech recognition Robustness Data models Adversarial machine learning Convolutional neural networks Low latency communication Testing

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Robust Video Watermarking network Based on Channel Spatial Attention

Robust Video Watermarking Network Based on Channel Spatial A...

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International Joint Conference on Neural networks (IJCNN)

作者： Jian Li Tao Zuo Bin Ma Chunpeng Wang Peng Zhang Huanhuan Zhao Zhengzhong Zhao 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

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Robust video watermarking refers to the ability to extract the originally embedded watermark information from a video even after malicious modifications and attacks. Currently, traditional watermarking methods have the drawback of lacking robustness against multiple watermark attacks simultaneously. Neural network-based approaches have not fully considered the multi-scale features of videos and tend to lose information during the fusion of scale features. Therefore, we propose a video watermarking scheme based on Channel Spatial Attention. Our model can extract feature information at different scales, allowing the watermark to adapt to features of different scales in the video. Through a series of comparative experiments, our method has shown significant improvements over traditional video watermarking methods and deep learning-based video watermarking models.

关键词： Adaptation models Convolution Video sequences Neural networks Watermarking Interference Feature extraction

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SGNet: A Multi-Scale Method for Steel Surface Defect Detection

SGNet: A Multi-Scale Method for Steel Surface Defect Detecti...

引用

Smart World Congress (SWC), IEEE

作者： Shanshan Liu Xiangzhi Liu Jiazheng Man Bingyang Cao Yunfeng Dong Xiaoming Wu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The large variation in target scales within steel surface defect images often results in low detection accuracy. To address this issue, we have developed a multi-scale defect detection method named SGNet. Initially, we introduced a Self-Guided Bi-Level Routing Transformer (SG-BiFormer) as the backbone network of our model. SG-BiFormer utilizes a self-guided strategy to model global information and dynamically reallocates tokens based on the importance of image regions, increasing token quantity in significant areas to preserve fine-grained details and reducing it in less critical areas. This strategy significantly enhances the accuracy and efficiency of the model in handling multi-scale targets. Subsequently, we incorporated Grouped Separable Convolutions (GSConv) to manage local information of multi-scale features. GSConv automatically adjusts the size and shape of the convolution kernels according to different input features. This design not only improves global information processing capabilities but also ensures sensitive and precise detail capture. Lastly, we employed MPDIoU as the loss function for our model, which optimizes corner positioning to tackle the challenges of regression tasks, substantially improving the accuracy of the model in handling complex defects. Experimental evidence confirms the effectiveness of this method. Our SGNet achieved a 77.1% mAP on the NEU-DET dataset, demonstrating outstanding performance.

关键词： Visualization Accuracy Shape Detectors Transformers Routing Feature extraction Steel Kernel Defect detection

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Cross-Chain Asset Transaction Method Based on Decentralized Digital Identity

Cross-Chain Asset Transaction Method Based on Decentralized ...

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Blockchain technology and Information security (ICBCTIS), International Conference on

作者： Ruiyao Zhou Shuhui Zhang Lianhai Wang Shujiang Xu Wei Shao Ting Gao Ruixue Kuang 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

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

The swift advancement of blockchain technology has improved the transfer of value and assets between various blockchain systems. Nonetheless, the differing underlying architectures of these platforms have impeded effective value transfers, leading to the development of cross-chain technology. Cross-chain transactions face complexities due to varied user identity definitions, methods, and authentication mechanisms across blockchain networks. To address these challenges, this study optimizes identity identification and authentication methods with a privacy-preserving solution for cross-chain asset transactions. This enhances the identity management mechanism, safeguarding user privacy. Initially using ring signature algorithms to anonymize user identities, the study now employs Decentralized Identity (DID) and Verifiable Credentials (VC) for unified identity identification and authentication across chains. This approach grants users control over their identities, removes blockchain barriers, and adapts to multiple scenarios. Comparative analysis includes gas consumption, transaction delays, and performance evaluations of both solutions.

关键词： Performance evaluation Privacy Refining Authentication Information security Blockchains Fraud Relays Protection Interoperability

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HyperPipe: Bridging Heterogeneous GPUs for Accelerated Large-Scale Model Training

HyperPipe: Bridging Heterogeneous GPUs for Accelerated Large...

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International Conference on Computer Supported Cooperative Work in Design

作者： Fulai Liul Chuantao Li Zhigang Zhao Chunxiao Wang Jidong Huo Jintao Li Lianhui Xiao Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China 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

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

To enhance the performance of pre-trained foundation models in various natural language processing tasks, the scale of these models has been steadily increasing in recent years, driven by advancements high-performance computing hardware. However, the presence of diverse computing devices in data centers and the varying computational demands of different tasks make it challenging for a single type of high-performance G PU to suffice for the large-scale training of foundation models' and lower-performance GPUs often remain underutilized. Therefore, optimizing resource a llocati on and improving training efficiency have become a critical challenge. To address these issues, this study proposes a hybrid parallel training framework for heterogeneous computing devices, named HyperPipe. This framework automatically selects the optimal hybrid parallel strategy based on the performance characteristics of the devices, thereby achieving efficient resource utilization and acceleration training to meet the computational demands of large-scale FMs. Experimental results demonstrate that in a heterogeneous computing environment, training large models like GPT-3-1.3B with HyperPipe achieves significant acceleration, delivering speed-ups ranging from 1.6 x to 2.8 x compared to previous methods.

关键词： Training Performance evaluation Tensors Foundation models Computational modeling Heuristic algorithms Parallel processing Heterogeneous networks Resource management Optimization

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Infringement Detection and Traceability Scheme Based on Ciphertext Feature Extraction

Infringement Detection and Traceability Scheme Based on Ciph...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Lianhai Wang Xinlei Wang Tianrui Liu Yingxiaochun Wang Qi 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 Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The rapid growth of healthcare data transactions has fostered win-win developments in healthcare technology innovation and economic benefits. However, the issue of secondary transactions makes data ownership vulnerable to infringement due to the easily replicable nature of the data. As the importance of ownership and privacy increases, more and more users protect the privacy of data through encryption methods, making it impossible for existing methods to make infringement judgments on ciphertext data. To solve this problem, we propose an infringement detection and traceability scheme based on feature extraction of ciphertext data. Firstly, the characteristics of fully homomorphic encryption are utilized to obtain data features in the ciphertext state, and the plaintext state of these features is used as the basis for infringement judgment. Additionally, during the data transaction process, the ownership registration of the sales data is verified through smart contracts to ensure the effective association between the transaction data and the seller’s address. The experimental results show that the scheme can accurately determine infringement while protecting data security and privacy.

关键词： Economics Data privacy Technological innovation Data security Smart contracts Cyberspace Medical services Data science Feature extraction Homomorphic encryption

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Data-Driven Active Power Dispatch for Wind Farms 13

Data-Driven Active Power Dispatch for Wind Farms

引用

13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Jiao, Xuguo Zhang, Daoyuan Wang, Xin Ma, Zhaoxing Zhang, Zhenyong Liu, Wenfeng School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China School of Civil Engineering Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9798350361674

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method. © 2024 IEEE.

关键词： Cost functions

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Research on Key Methods for Extracting High-Quality Chinese Corpus Based on Common Crawl

Research on Key Methods for Extracting High-Quality Chinese ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Lianhui Xiao Zhigang Zhao Chunxiao Wang Jian Zhang Fulai Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) (Shandong Academy of Sciences) Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

With the rapid development of large-scale Chinese pre-trained language models and natural language processing technologies, the demand for large, high-quality Chinese corpora is significantly increasing. However, current Chinese corpora often fail to meet the demands of high-quality training due to their limited size and substandard quality. This paper introduces a method to extract high-quality Chinese corpora based on the Common Crawl dataset, enhancing existing quality filtering and deduplication methods. In terms of deduplication, this paper enhances the Simhash algorithm to tackle the issue of feature word co-occurrence effectively and introduces a deduplication method based on sentence clusters. Additionally, this paper proposes an offensive speech detection method, ODet, based on prefix tuning and prompt learning. ODet has demonstrated superior performance compared to existing offensive speech detection methods and has been successfully applied in the corpus processing workflow. We successfully extracted a 500GB high-quality Chinese corpus, CHCorpus, employing this approach. Furthermore, we applied a perplexity calculation method to segment CHCorpus into head, middle, and tail sections. The experimental outcomes indicate that CHCorpus excels in perplexity assessments, several CLUE benchmark tasks, and text summarization, surpassing Wikipedia, CLUECorpus2020, and CCNet corpora. This research provides a robust data foundation for training large-scale Chinese pre-trained models, significantly improving their robustness and security.

关键词： Voice activity detection Training Filtering Federated learning Text summarization Robustness Natural language processing Internet Online services Tuning

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A Deep Reinforcement Learning-based Collaborative Edge Caching Approach for AIoT

A Deep Reinforcement Learning-based Collaborative Edge Cachi...

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International Conference on Computer Supported Cooperative Work in Design

作者： Ze Liu Hao Hao Wei Zhang 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

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Edge caching for Artificial Intelligence of Things (AIoT) data is crucial for reducing cloud load and providing real-time services to AIoT users. AIoT caching faces challenges due to dynamic, context-sensitive data demands. Static methods based on popularity prediction struggle with rapid changes, while fully dynamic approaches may miss long-term trends, reducing effectiveness. To address this, we propose a novel approach that combines region segmentation with Temporal Convolutional networks (TCN) and multi-head attention mechanisms for ac-curate content request prediction. This approach captures both spatial and temporal patterns in content demand. We optimize content decision-making through a Constrained Markov Decision Process (CMDP) and Deep Q-network (DQN), enhancing edge hit rates and reducing access latency. Furthermore, our multi-agent learning framework enables centralized training with decentral-ized execution, facilitating efficient content caching across edge nodes. Experimental results show that our approach outperforms baseline methods in edge hit rate and content access latency, offering a more efficient and adaptive solution for AIoT data caching in dynamic environments.

关键词： Training Attention mechanisms Markov decision processes Heuristic algorithms Decision making Prediction algorithms Market research Real-time systems Internet of Things Artificial intelligence

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Optimization Method for Cross-Domain Heterogeneous Storage System Management Based on Improved NSGA-III

Optimization Method for Cross-Domain Heterogeneous Storage S...

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International Conference on Computer Supported Cooperative Work in Design

作者： Qiuyue Liu Chunxiao Wang Yinglong Wang Zhigang Zhao Shuchang Zang Huagang Wen 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

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Given the diversity and geographic distribution of data resources across multiple data centers, achieving unified storage and resource management presents significant challenges, complicating efforts to meet the demands for efficient cross-domain distributed data access. To address these challenges, this study proposes a cross-domain heterogeneous storage cluster architecture. Leveraging Alluxio as the foundational storage layer for data-intensive applications, this architecture facilitates unified management across various storage systems. For the storage system selection problem, this paper adopts a multi-objective approach with minimum cost, maximum reliability, shortest access distance, and load balancing as the target functions. An improved NSGA-III algorithm, incorporating generalized inverse learning and adaptive evolutionary mechanisms, is proposed for intelligently selecting the optimal storage scheme. The reasonableness and effectiveness of the improved algorithm are validated through a comparative analysis of the convergence and diversity metrics of the original and improved NSGA-III algorithms on benchmark problems, along with assessments of each objective's adaptability in real-world application scenarios.

关键词： Measurement Data centers Costs Federated learning Distributed databases Optimization methods Computer architecture Load management Resource management Reliability

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