IEEE Journal on Emerging and Selected Topics in Circuits and Systems最新文献

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Information for Authors

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-12-13 DOI: 10.1109/JETCAS.2024.3502893

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IEEE Journal on Emerging and Selected Topics in Circuits and Systems Publication Information 电气和电子工程师学会电路与系统新专题与选题期刊》出版信息

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-12-13 DOI: 10.1109/JETCAS.2024.3502897

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Erratum to “A Reconfigurable Spatial Architecture for Energy-Efficient Inception Neural Networks”

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-12-13 DOI: 10.1109/JETCAS.2024.3464190

Lichuan Luo;Wang Kang;Junzhan Liu;He Zhang;Youguang Zhang;Dijun Liu;Peng Ouyang

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IEEE Circuits and Systems Society Information 电气和电子工程师学会电路与系统协会信息

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-12-13 DOI: 10.1109/JETCAS.2024.3502895

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Guest Editorial: Toward Trustworthy AI: Advances in Circuits, Systems, and Applications 客座编辑：迈向可信赖的人工智能：电路、系统和应用的进展

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-12-13 DOI: 10.1109/JETCAS.2024.3497232

Shih-Hsu Huang;Pin-Yu Chen;Stjepan Picek;Chip-Hong Chang

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Decision Guided Robust DL Classification of Adversarial Images Combining Weaker Defenses

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-11-13 DOI: 10.1109/JETCAS.2024.3497295

Shubhajit Datta;Manaar Alam;Arijit Mondal;Debdeep Mukhopadhyay;Partha Pratim Chakrabarti

{"title":"Decision Guided Robust DL Classification of Adversarial Images Combining Weaker Defenses","authors":"Shubhajit Datta;Manaar Alam;Arijit Mondal;Debdeep Mukhopadhyay;Partha Pratim Chakrabarti","doi":"10.1109/JETCAS.2024.3497295","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3497295","url":null,"abstract":"Adversarial examples make Deep Learning (DL) models vulnerable to safe deployment in practical systems. Although several techniques have been proposed in the literature, defending against adversarial attacks is still challenging. The current work identifies weaknesses of traditional strategies in detecting and classifying adversarial examples. To overcome these limitations, we carefully analyze techniques like binary detector and ensemble method, and compose them in a manner which mitigates the limitations. We also effectively develop a re-attack strategy, a randomization technique called RRP (Random Resizing and Patch-removing), and a rule-based decision method. Our proposed method, BEARR (Binary detector with Ensemble and re-Attacking scheme including Randomization and Rule-based decision technique) detects adversarial examples as well as classifies those examples with a higher accuracy compared to contemporary methods. We evaluate BEARR on standard image classification datasets: CIFAR-10, CIFAR-100, and tiny-imagenet as well as two real-world datasets: plantvillage and chest X-ray in the presence of state-of-the-art adversarial attack techniques. We have also validated BEARR against a more potent attacker who has perfect knowledge of the protection mechanism. We observe that BEARR is significantly better than existing methods in the context of detection and classification accuracy of adversarial examples.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"758-772"},"PeriodicalIF":3.7,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Systematical Evasion From Learning-Based Microarchitectural Attack Detection Tools

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-11-04 DOI: 10.1109/JETCAS.2024.3491497

Debopriya Roy Dipta;Jonathan Tan;Berk Gulmezoglu

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SecureComm: A Secure Data Transfer Framework for Neural Network Inference on CPU-FPGA Heterogeneous Edge Devices SecureComm：用于 CPU-FPGA 异构边缘设备神经网络推理的安全数据传输框架

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-11-04 DOI: 10.1109/JETCAS.2024.3491169

Tian Chen;Yu-An Tan;Chunying Li;Zheng Zhang;Weizhi Meng;Yuanzhang Li

{"title":"SecureComm: A Secure Data Transfer Framework for Neural Network Inference on CPU-FPGA Heterogeneous Edge Devices","authors":"Tian Chen;Yu-An Tan;Chunying Li;Zheng Zhang;Weizhi Meng;Yuanzhang Li","doi":"10.1109/JETCAS.2024.3491169","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3491169","url":null,"abstract":"With the increasing popularity of heterogeneous computing systems in Artificial Intelligence (AI) applications, ensuring the confidentiality and integrity of sensitive data transferred between different elements has become a critical challenge. In this paper, we propose an enhanced security framework called SecureComm to protect data transfer between ARM CPU and FPGA through Double Data Rate (DDR) memory on CPU-FPGA heterogeneous platforms. SecureComm extends the SM4 crypto module by incorporating a proposed Message Authentication Code (MAC) to ensure data confidentiality and integrity. It also constructs smart queues in the shared memory of DDR, which work in conjunction with the designed protocols to help schedule data flow and facilitate flexible adaptation to various AI tasks with different data scales. Furthermore, some of the hardware modules of SecureComm are improved and encapsulated as independent IPs to increase their versatility beyond the scope of this paper. We implemented several ARM CPU-FPGA collaborative AI applications to justify the security and evaluate the timing overhead of SecureComm. We also deployed SecureComm to non-AI tasks to demonstrate its versatility, ultimately offering suggestions for its use in tasks of varying data scales.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"811-822"},"PeriodicalIF":3.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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On Function-Coupled Watermarks for Deep Neural Networks

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-10-30 DOI: 10.1109/JETCAS.2024.3476386

Xiangyu Wen;Yu Li;Wei Jiang;Qiang Xu

{"title":"On Function-Coupled Watermarks for Deep Neural Networks","authors":"Xiangyu Wen;Yu Li;Wei Jiang;Qiang Xu","doi":"10.1109/JETCAS.2024.3476386","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3476386","url":null,"abstract":"Well-performed deep neural networks (DNNs) generally require massive labeled data and computational resources for training. Various watermarking techniques are proposed to protect such intellectual properties (IPs), wherein the DNN providers can claim IP ownership by retrieving their embedded watermarks. While promising results are reported in the literature, existing solutions suffer from watermark removal attacks, such as model fine-tuning, model pruning, and model extraction. In this paper, we propose a novel DNN watermarking solution that can effectively defend against the above attacks. Our key insight is to enhance the coupling of the watermark and model functionalities such that removing the watermark would inevitably degrade the model’s performance on normal inputs. Specifically, on one hand, we sample inputs from the original training dataset and fuse them as watermark images. On the other hand, we randomly mask model weights during training to distribute the watermark information in the network. Our method can successfully defend against common watermark removal attacks, watermark ambiguity attacks, and existing widely used backdoor detection methods, outperforming existing solutions as demonstrated by evaluation results on various benchmarks. Our code is available at: \u0000<uri>https://github.com/cure-lab/Function-Coupled-Watermark</uri>\u0000.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"608-619"},"PeriodicalIF":3.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738841","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Extracting DNN Architectures via Runtime Profiling on Mobile GPUs 在移动 GPU 上通过运行时剖析提取 DNN 架构

IF 3.7 2区工程技术

IEEE Journal on Emerging and Selected Topics in Circuits and Systems Pub Date : 2024-10-30 DOI: 10.1109/JETCAS.2024.3488597

Dong Hyub Kim;Jonah O’Brien Weiss;Sandip Kundu

{"title":"Extracting DNN Architectures via Runtime Profiling on Mobile GPUs","authors":"Dong Hyub Kim;Jonah O’Brien Weiss;Sandip Kundu","doi":"10.1109/JETCAS.2024.3488597","DOIUrl":"https://doi.org/10.1109/JETCAS.2024.3488597","url":null,"abstract":"Deep Neural Networks (DNNs) have become invaluable intellectual property for AI providers due to advancements fueled by a decade of research and development. However, recent studies have demonstrated the effectiveness of model extraction attacks, which threaten this value by stealing DNN models. These attacks can lead to misuse of personal data, safety risks in critical systems, and the spread of misinformation. This paper explores model extraction attacks on DNN models deployed on mobile devices, using runtime profiles as a side-channel. Since mobile devices are resource constrained, DNN deployments require optimization efforts to reduce latency. The main hurdle in extracting DNN architectures in this scenario is that optimization techniques, such as operator-level and graph-level fusion, can obfuscate the association between runtime profile operators and their corresponding DNN layers, posing challenges for adversaries to accurately predict the computation performed. To overcome this, we propose a novel method analyzing GPU call profiles to identify the original DNN architecture. Our approach achieves full accuracy in extracting DNN architectures from a predefined set, even when layer information is obscured. For unseen architectures, a layer-by-layer hyperparameter extraction method guided by sub-layer patterns is introduced, also achieving high accuracy. This research achieves two firsts: 1) targeting mobile GPUs for DNN architecture extraction and 2) successfully extracting architectures from optimized models with fused layers.","PeriodicalId":48827,"journal":{"name":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","volume":"14 4","pages":"620-633"},"PeriodicalIF":3.7,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0