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D4A: An efficient and effective defense across agnostic adversarial attacks

Neural networks 2025年 183卷 106938页

作者： Li, Xianxian Gan, Zeming Bai, Yan Su, Linlin Li, De Wang, Jinyan Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China School of Engineering and Technology University of Washington Tacoma TacomaWA United States

Recent studies show that Graph Neural networks (GNNs) are vulnerable to structure adversarial attacks, which draws attention to adversarial defenses in graph data. Previous defenses designed heuristic defense strategies for specific attacks or graph properties, and are no longer sufficiently robust across all these attacks. To address this problem, we discuss the abnormal behaviors of GNNs in structure perturbations from a posterior distribution perspective. We suggest that the structural vulnerability of GNNs stems from their dependence on local graph smoothing, which can also lead to unfitting — a first-found phenomenon specific to the graph domain. We demonstrate that abnormal behaviors, except for unfitting, can attribute to a posterior distribution shift. To intrinsically prevent the occurrence of abnormal behaviors, we first propose smooth-less message passing to enhance the tolerance with respect to structure perturbations, while significantly mitigating the unfitting. We also propose the distribution shift constraint to restrict other abnormal behaviors of our model. Our approach is evaluated on six different datasets across over four kinds of attacks and compared to 11 representative baselines. The experimental results show that our method improves the defense performance across various attacks, and provides a great trade-off between accuracy and adversarial robustness. © 2024 Elsevier Ltd

关键词： Generative adversarial networks

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DetecVFuzz: Enhancing security in Consumer Electronic Devices Through Scalable Vulnerability Testing of Virtual Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Xiao, Xi Guo, Yongjian Jolfaei, Alireza Chen, Chuan Haghighi, Mohammad Sayad Wen, Sheng Lin, Yuanyi Tsinghua University Shenzhen International Graduate School Shenzhen China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education China Peng Cheng Laboratory Guangdong Shenzhen China Flinders University College of Science and Engineering SA5042 Australia Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Ministry of Education China Qilu University of Technology Shandong Academy of Sciences National Supercomputer Center in Jinan Jinan China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia

The interconnection facilitated by consumer electronics has led to the creation of a complex network of devices. Testing efficiency of such consumer electronic devices can be improved through hardware emulation. QEMU is a classical emulator for simulation and testing. Fuzzing is a promising solution to test the security of such devices, which has become pivotal in identifying device vulnerability. However, Qtest libfuzzer, the QEMU native fuzzer, requires manual writing of stub code, which makes conducting the fuzzing process difficult and yields poor performance. To address these challenges in virtual device security analysis, we propose DetecVFuzz, a novel automatic fuzzing framework based on QEMU-Qtest, aiming to find vulnerabilities in virtual PCI devices, including USB, audio, network, etc. We inject mutated inputs into nested data structures as parameters of memory-related APIs to ensure the stable operation of VM procedures. We implemented DetecVFuzz and evaluated it on different virtual devices, such as virtual memory USB stack and drivers. In preliminary evaluations on nine recent versions of the QEMU, we found 15 new memory errors in virtual devices. In general, DetecVFuzz achieves 89.37% line coverage and 78.65% branch coverage in virtual devices. We identified 26 new crashes/errors, three of which were unseen new bugs. © 1975-2011 IEEE.

关键词： Virtual addresses

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Mitigating label-Noise for Facial Expression Recognition in the Wild

Mitigating Label-Noise for Facial Expression Recognition in ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Yan, Huan Gu, Yu Zhang, Xiang Wang, Yantong Ji, Yusheng Ren, Fuji School of Computer and Information Hefei University of Technology S2AC Lab China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China National Institute of Informatics Tokushima University Japan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

label-noise constitutes a major challenge for facial expression recognition in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, we propose a simple but effective label-noise Robust network (LRN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, LRN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, LRN utilizes a confidence-based label-free loss to extract compact intra-class feature representations under label noise while preserving the intrinsic inter-class relationships. Experiments on three in-the-wild facial expression datasets demonstrates the superiority of our method. © 2022 IEEE.

关键词： Face recognition

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Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications

Map2Schedule: An End-to-End Link Scheduling Method for Urban...

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IEEE International Conference on Communications (ICC)

作者： Lihao Zhang Haijian Sun Jin Sun Ramviyas Parasuraman Yinghui Ye Rose Qingyang Hu School of Electrical and Computer Engineering University of Georgia Athens GA USA School of Computing University of Georgia Athens GA USA Shanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications China Electrical and Computer Engineering Department at Utah State University Logan UT USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

关键词： Wireless communication Accuracy Processor scheduling Computational modeling System performance Urban areas Vehicular ad hoc networks

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AirDIV: Over-the-Air Cloud-Fog Data Integrity Verification Scheme for Industrial Cyber-Physical Systems

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IEEE Journal on Selected Areas in Communications 2025年

作者： Zhao, Yao Xiang, Yong Uddin, Md Palash Zhang, Yushu Liu, Lu Liu, Yansong Gao, Longxiang Deakin University School of Information Technology VIC Australia Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China University of Exeter Department of Computer Science Exeter United Kingdom RWTH Aachen University Department of Computer Science Aachen Germany Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

Industrial Cyber-Physical Systems (ICPSs) have been motivating various Industry 4.0 endeavours, particularly with the integration of fog computing. Cloud-fog data caching paradigms, as supportive elements of ICPSs, have been adopted to cache user data, catering to diverse ICPS requirements such as data sensitivity and reduced access latency. In this hierarchical caching context, ensuring Cloud-Fog Data Integrity (CFDI) is crucial for maintaining the consistent functionality of ICPSs. Existing solutions primarily focus on examining the integrity of data cached solely on either cloud or fog nodes. However, cloud-cached data and fog-cached data are tightly coupled and should be considered simultaneously when checking data integrity. In this work, we introduce an over-the-air CFDI verification scheme, namely AirDIV, with a high accuracy and security guarantee. Instead of aggregating integrity proofs after proof transmission, AirDIV completes proof aggregation and transmission over the air for efficiency improvement. To enhance practicability, we derive adjustable parameters and formulate an optimization problem to minimize over-the-air aggregation errors. Furthermore, with an effective proof generation method, AirDIV can defend against two common attacks, i.e., replay and forge attacks. We provide a theoretical analysis of AirDIV's correctness, accuracy and security, while conducting extensive experiments on both simulated and real platforms to validate its efficiency. © 1983-2012 IEEE.

关键词： Cache memory

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SML: A Backdoor Defense for Non-Intrusive Speech Quality Assessment via Semi-Supervised and Multi-Task Learning

SML: A Backdoor Defense for Non-Intrusive Speech Quality Ass...

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

作者： Ying Ren Wenjie Zhang Jiahong Ye Jie Li Diqun Yan Bin Ma Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Digital Technology and Engineering Ningbo University of Finance and Economics Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Non-intrusive speech quality assessment (NISQA) is widely used in speech downstream tasks due to its ability to predict the quality of speech without a reference speech. However, few researchers have focused on the backdoor security of NISQA. Despite the backdoor defenses have been extensively studied to mitigate the threat of maliciously modifications in deep neural networks. In particular, semi-supervised based backdoor defenses have excellent defensive performance by depriving backdoor attacks of their most essential need. But these defense methods rely on data-augmentation consistency and thus cannot be applied to NISQA. In this work, we propose a backdoor defense based on semi-supervised and multi-task learning (SML). Semi-supervised learning is based on the simple assumption that the same input should be as consistent as possible in two similar models. Multi-task learning further improves the prediction performance of mean opinion score (MOS) by learning the tasks of perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI) and speech distortion index (SDI). Extensive experiments involving five backdoor defenses against five backdoor attacks on two benchmark datasets demonstrate the superiority of our SML approach.

关键词： Acoustic distortion Artificial neural networks Semisupervised learning Benchmark testing Multitasking Acoustics Quality assessment security Indexes Speech processing

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Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yanzuo Lu Manlin Zhang Andy J Ma Xiaohua Xie Jianhuang Lai School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Pazhou Lab (HuangPu) Guangzhou China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Diffusion model is a promising approach to image generation and has been employed for Pose-Guided Person Image Synthesis (PGPIS) with competitive performance. While existing methods simply align the person appearance to the target pose, they are prone to overfitting due to the lack of a high-level semantic understanding on the source person image. In this paper, we propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for PGPIS. In the absence of image-caption pairs and textual prompts, we de-velop a novel training paradigm purely based on images to control the generation process of a pre-trained text-to-image diffusion model. A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt. This allows for the decoupling of fine-grained appearance and pose information controls at different stages, and thus circumventing the potential over-fitting problem. To generate more realistic texture details, a hybrid- granularity attention module is proposed to encode multi-scale fine-grained appearance features as bias terms to augment the coarse-grained prompt. Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method over the state of the arts for PGPIS. Code is available at https://***/YanzuoLu/CFLD.

关键词： Training Image synthesis Semantics Text to image Process control Diffusion models Generators

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RoKEPG: RoBERTa and Knowledge Enhancement for Prescription Generation of Traditional Chinese Medicine

RoKEPG: RoBERTa and Knowledge Enhancement for Prescription G...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hua Pu Jiacong Mi Shan Lu Jieyue He School of Computer Science and Engineering Key Lab of Computer Network and Information Integration MOE Southeast University Nanjing China Nanjing Fenghuo Tiandi Communication Technology Co. Ltd Nanjing China

Traditional Chinese medicine (TCM) prescription is the most critical form of TCM treatment, and uncovering the complex nonlinear relationship between symptoms and TCM is of great significance for clinical practice and assisting physicians in diagnosis and treatment. Although there have been some studies on TCM prescription generation, these studies consider a single factor and directly model the symptom-prescription generation problem mainly based on symptom descriptions, lacking guidance from TCM knowledge. To this end, we propose a RoBERTa and Knowledge Enhancement model for Prescription Generation of Traditional Chinese Medicine (RoKEPG). RoKEPG is firstly pre-trained by our constructed TCM corpus, followed by fine-tuning the pre-trained model, and the model is guided to generate TCM prescriptions by introducing four classes of knowledge of TCM through the attention mask matrix. Experimental results on the publicly available TCM prescription dataset show that RoKEPG improves the F 1 metric by about 2% over the baseline model with the best results.

关键词：

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

EventTrojan: Manipulating Non-Intrusive Speech Quality Asses...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Ying Ren Kailai Shen Zhe Ye Diqun Yan Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Juphoon System Software Co. Ltd Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods.

关键词： Measurement Resistance Adaptation models Multimedia systems Current measurement Artificial neural networks Benchmark testing

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Detection method of trusted blockchain link flood attack based on trusted execution environment

Detection method of trusted blockchain link flood attack bas...

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information Technology: IoT and Smart City (ITIoTSC), International Conference on

作者： Ruixue Kuang Lianhai Wang Shuhui Zhang Shujiang Xu Wei Shao Qizheng Wang 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 Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (数字)9798350328349

ISBN: (纸本)9798350328356

In order to improve the detection accuracy and improve the detection speed, this paper designs a detection method of trusted blockchain link flood attack based on the trusted execution environment. First, above all, analyze the trusted block chain structure, then design the trusted block chain link flood attack detection algorithm, and finally the trusted execution environment is used to realize the trusted block chain link flood attack detection. The experimental results prove that the false alarm rate and leakage detection rate of the detection method of the flood flood attack designed based on the trusted execution environment are both low, with the lowest value only 3% and 0.98%. The detection accuracy is high, the detection time is short, and the detection can be completed in at least 13s. It can be proved that the performance of this design method is better and has certain application value.

关键词： Accuracy information resources Smart cities computer hacking Design methodology Blockchains Floods

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