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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Mengyuan Zhang Shunhui Ji Hanbo Cai Hai Dong Pengcheng Zhang Yunhe Li Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Computing Technologies RMIT University Melbourne Australia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

With the growing prevalence of deep learning in the speech area, speech recognition, voice control, and related applications have become integral parts of people's lives. However, the rise of malicious third-party platforms has introduced significant security concerns, particularly through backdoor attacks. These attacks implant triggers that manipulate speech recognition models to produce specific labels, thereby compromising the system's integrity. Studying speech backdoor attacks is crucial for evaluating the security of speech recognition software, and iden-tifying and addressing potential vulnerabilities. Existing methods for speech backdoor attacks usually employ fixed perturbations as triggers. However, these perturbations may be discernible to the human ear, making them easily detectable. To address this issue, we propose a frequency domain-embedded backdoor attack method based on echo hiding. Echo hiding is a steganography technique based on audio. This method embeds hidden information into the frequency spectrum of the echo signal, leveraging the masking property of the human auditory system. It is difficult to arouse suspicion or detect the presence of hidden information since echo is perceived as a natural phenomenon in auditory perception. Furthermore, it does not cause a significant decrease in audio quality. Experimental results show the effectiveness of our method in different settings.

关键词： Training Steganography Perturbation methods Frequency-domain analysis Speech recognition Ear Implants

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ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation

arXiv

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arXiv 2025年

作者： Bian, Qingtian Vinícius de Carvalho, Marcus Li, Tieying Xu, Jiaxing Fang, Hui Ke, Yiping College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Northeastern University China Research Institute for Interdisciplinary Sciences Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it. Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings. Another challenge lies in aligning the domain-specific and cross-domain sequences. Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains. In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). Specifically, leveraging LoRA’s effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation. First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics. Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain. Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths. Experimental results on three datasets demonstrate that our approach outperforms other CDSR counterparts by a large margin. The codes are available in https://***/DiMarzioBian/ABXI. © 2025, CC BY.

关键词： Knowledge transfer

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Scribble-supervised semantic segmentation with prototype-based feature augmentation 24

Scribble-supervised semantic segmentation with prototype-bas...

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Proceedings of the 41st International Conference on Machine Learning

作者： Guiyang Chan Pengcheng Zhang Hai Dong Shunhui Ji Bainian Chen Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China and College of Computer Science and Software Engineering Hohai University Nanjing China School of Computing Technologies RMIT University Melbourne Australia

Scribble-supervised semantic segmentation presents a cost-effective training method that utilizes annotations generated through scribbling. It is valued in attaining high performance while minimizing annotation costs, which has made it highly regarded among researchers. Scribble supervision propagates information from labeled pixels to the surrounding unlabeled pixels, enabling semantic segmentation for the entire image. However, existing methods often ignore the features of classified pixels during feature propagation. To address these limitations, this paper proposes a prototype-based feature augmentation method that leverages feature prototypes to augment scribble supervision. Experimental results demonstrate that our approach achieves state-of-the-art performance on the PASCAL VOC 2012 dataset in scribble-supervised semantic segmentation tasks. The code is available at https://***/TranquilChan/PFA.

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Validating SMT Solvers via Skeleton Enumeration Empowered by Historical Bug-Triggering Inputs

Validating SMT Solvers via Skeleton Enumeration Empowered by...

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International Conference on Software engineering (ICSE)

作者： Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Huazhong University of Science and Technology China

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.

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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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MODFuzz: A Multi-Objective Directed Fuzzer for USB Drivers

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IEEE Internet of Things Journal 2025年

作者： Zhou, Yilin Peng, Guojun Wang, Xingliang Wang, Chenyang Li, Zichuan Liu, Side Wang, Yanhao Yang, Xiuzhang Fu, Jianming Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Indiana University Bloomington BloomingtonIN United States Nio Inc Beijing China Guizhou University Guizhou Big Data Academy Guiyang China

USB interfaces have become ubiquitous in various Internet of Things (IoT) devices, all adhering to the same USB protocol. While enhancing convenience, they also widen the potential attack surface. Fuzzing is a proactive way to identify potential security threats for USB drivers. However, existing USB driver fuzzers primarily prioritize the code coverage of USB drivers, leading to a significant waste of computational resources on irrelevant code segments. To this end, we combine directed fuzzing and USB driver fuzzing for the first time, and present MODFuzz, a pioneering multi-objective directed fuzzing method for USB drivers. MODFuzz autonomously locates the most vulnerable parts within USB drivers, concentrating fuzzing efforts on these areas. Diverging from the existing directed fuzzers, MODFuzz employs a dynamic direction instead of predetermined addresses to guide the fuzzing campaign toward the triggered execution traces with a greater probability of containing vulnerabilities. MODFuzz outperforms the strong baseline in terms of execution speed (about 14% improvement) and crash generation capabilities (about 69% improvement). Meanwhile, we found six previously unknown bugs (all confirmed and assigned vulnerability IDs) in Linux kernel v6.4.10 and received acknowledgment from Red Hat. © 2025 IEEE.

关键词： Linux

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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TFGDA: exploring topology and feature alignment in semi-supervised graph domain adaptation through robust clustering 24

TFGDA: exploring topology and feature alignment in semi-supe...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jun Dan Weiming Liu Chunfeng Xie Hua Yu Shunjie Dong Yanchao Tan Zhejiang University Queen Mary University of London Dalian University of Technology Shanghai Jiao Tong University Fuzhou University and Engineering Research Center of Big Data Intelligence Ministry of Education and Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9798331314385

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a label-scarce source graph. However, most existing studies primarily concentrate on aligning feature distributions directly to extract domain-invariant features, while ignoring the utilization of the intrinsic structure information in graphs. Inspired by the significance of data structure information in enhancing models' generalization performance, this paper aims to investigate how to leverage the structure information to assist graph transfer learning. To this end, we propose an innovative framework called TFGDA. Specially, TFGDA employs a structure alignment strategy named STSA to encode graphs' topological structure information into the latent space, greatly facilitating the learning of transferable features. To achieve a stable alignment of feature distributions, we also introduce a SDA strategy to mitigate domain discrepancy on the sphere. Moreover, to address the overfitting issue caused by label scarcity, a simple but effective RNC strategy is devised to guide the discriminative clustering of unlabeled nodes. Experiments on various benchmarks demonstrate the superiority of TFGDA over SOTA methods.

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CIMI4D: A Large Multimodal Climbing Motion dataset under Human-scene Interactions

CIMI4D: A Large Multimodal Climbing Motion Dataset under Hum...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ming Yan Xin Wang Yudi Dai Siqi Shen Chenglu Wen Lan Xu Yuexin Ma Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University

Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off- grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to capture because of its complex back poses, intricate human-scene interactions, and difficult global localization. The research community does not have an indepth understanding of the climbing action due to the lack of specific datasets. To address this limitation, we collect CIMI4D, a large rock Climbing Motion dataset from 12 persons climbing 13 different climbing walls. The dataset consists of around 180,000 frames of pose inertial measurements, LiDAR point clouds, RGB videos, high-precision static point cloud scenes, and reconstructed scene meshes. Moreover, we frame-wise annotate touch rock holds to facilitate a detailed exploration of human-scene interaction. The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four tasks which include human pose estimations (with/without scene constraints), pose prediction, and pose generation. The experimental results demonstrate that CIMI4D presents great challenges to existing methods and enables extensive research opportunities. We share the dataset with the research community in http://***/cimi4d/.

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Network Security Situation Assessment Method Based on Absorbing Markov Chain

Network Security Situation Assessment Method Based on Absorb...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Gao, Hongbin Wang, Shangxing Zhang, Hongbin Liu, Bin Zhao, Dongmei Liu, Zhen Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

This paper has a new network security evaluation method as an absorbing Markov chain-based assessment method. This method is different from other network security situation assessment methods based on graph theory. It effectively refinement issues such as poor objectivity of other methods, incomplete consideration of evaluation factors, and mismatching of evaluation results with the actual situation of the network. Firstly, this method collects the security elements in the network. Then, using graph theory combined with absorbing Markov chain, the threat values of vulnerable nodes are calculated and sorted. Finally, the maximum possible attack path is obtained by blending network asset information to determine the current network security status. The experimental results prove that the method fully considers the vulnerability and threat node ranking and the specific case of system network assets, which makes the evaluation result close to the actual network situation. © 2022 IEEE.

关键词： Network security

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