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arXiv 2025年

作者： Wang, Zhigang Fan, Shaojing Liu, Zhenguang Wu, Zheqi Wu, Sifan Jiao, Yingying College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatio-temporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset. © 2025, CC BY.

关键词： Spatio-temporal data

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Preventing the Popular Item Embedding Based Attack in Federated Recommendations

arXiv

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arXiv 2025年

作者： Zhang, Jun Li, Huan Rong, Dazhong Zhao, Yan Chen, Ke Shou, Lidan The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science Aalborg University Aalborg Denmark

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense. © 2025, CC BY.

关键词： Federated learning

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Multi-Grained Feature Pruning for Video-Based Human Pose Estimation

Multi-Grained Feature Pruning for Video-Based Human Pose Est...

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

作者： Zhigang Wang Shaojing Fan Zhenguang Liu Zheqi Wu Sifan Wu Yingying Jiao College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatiotemporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset.

关键词： Accuracy Pose estimation Semantics Signal processing algorithms Benchmark testing Signal processing Motion capture Computational efficiency Spatiotemporal phenomena Speech processing

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InTime: Towards Performance Predictability In Byzantine Fault Tolerant Proof-of-Stake Consensus

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Proceedings of the ACM on Management of data 2025年第1期3卷 1-27页

作者： Weijie Sun Zihuan Xu Wangze Ni Lei Chen The Hong Kong University of Science and Technology Hong Kong Hong Kong Shenzhen Institute of Computing Sciences Shenzhen China Zhejiang University Hangzhou China The State Key Laboratory of Blockchain and Data Security Hangzhou China & Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China The Hong Kong University of Science and Technology Hong Kong Hong Kong & HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

Performance predictability, ensuring low latency variability, is crucial for the reliability and efficiency of blockchain consensus. Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS) consensus aims to achieve stable transaction processing latency by scheduling block generation at consistent intervals. However, BFT-PoS's incentive mechanisms grant all transaction tips to the block proposer, which can be exploited by delaying proposals to gain extra Maximal Extractable Value (MEV) rewards, thus undermining performance predictability. Existing solutions impose penalties for delays but lack a standard for measuring the extra rewards from delays or fail in malicious *** paper introduces InTime, a novel approach to safeguard performance predictability in BFT-PoS by economically motivating timely block proposals. We first introduce the untimely MEV ratio, a reliable metric to measure the extra rewards gained from proposal delays, facilitating our countermeasures against deliberate delays. Furthermore, we propose the arrival rate incentive (ARI), aligning rewards with transaction arrival timing among nodes to reduce potential MEV manipulation. To make ARI robust against malicious behaviors, we establish a committee time witness (CTW) workflow to accurately gather and verify transaction arrival times. Extensive experiments demonstrate that InTime can effectively reduce latency variability by up to 95.9%.

关键词： PoS consensus blockchain incentive design

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Universal Low Bit-Rate Speech Steganalysis Integrating Domain-specific and Domain-shared Knowledge

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Tian, Hui Qiu, Yiqin Li, Haizhou Zhang, Xinpeng Vasilakos, Athanasios V. National Huaqiao University College of Computer Science and Technology Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China School of Data Science Chinese University of Hong Kong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering 119077 Singapore Fudan University School of Computer Science Shanghai200433 China University of Agder Center for AI Research Grimstad4879 Norway

Universal low bit-rate speech steganalysis is a cutting-edge research task addressing real-world application needs and has garnered significant attention recently. However, the existing methods are still inadequate in extracting available information from various steganographic domains and fail to deliver interpretable forensic results for specific steganographic domains containing embedded information. In view of this, we present a novel universal low bit-rate speech steganalysis approach that seamlessly combines domain-specific and domain-shared information, enabling comprehensive and effective speech steganography detection. This approach comprises two vital components: the Matching Identification Network (MIN) and the Content Alignment Network (CAN). The MIN incorporates three effective separable backbones for capturing informative domain-specific embeddings, inherently unveiling local and global dependencies. In this network, we also design a cross-domain matching module to establish correlations among steganographic domains, thereby enhancing detection performance through multi-domain collaboration and facilitating effective forensics for the embedded domains. Moreover, the CAN acquires more informative domain-shared embeddings by using a metric learning-based Siamese architecture to process pairs of naive and recompressed speech samples. Experimental results demonstrate that the presented method not only significantly surpasses the existing universal steganalysis methods, but also competes with or even surpasses dedicated steganalysis methods in certain cases. In addition, our method can provide accurate forensic results regarding the existence of hidden information within each steganographic domain without relevant supervisory information, marking a significant milestone in pursuit of speech steganalysis. The source code for this work will be publicly available on GitHub. © 2004-2012 IEEE.

关键词： Steganography

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When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?

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ACM Transactions on Software Engineering and Methodology 2025年第4期34卷

作者： Chen, Chong Su, Jianzhong Chen, Jiachi Wang, Yanlin Bi, Tingting Yu, Jianxing Wang, Yanli Lin, Xingwei Chen, Ting Zheng, Zibin Sun Yat-sen University Guangzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China University of Western Australia Perth Australia Zhejiang University Hangzhou China University of Electronic Science and Technology of China Chengdu China

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attention, showcasing great capabilities in code analysis tasks. In this article, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT's effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT's performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT's F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions;and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Empirical Study Smart Contracts Large Language Models ChatGPT Vulnerabilities

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Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are available at https://***/GXNU-ZhongLab/MAVLT. © 1991-2012 IEEE.

关键词： Semantics

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THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

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IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

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TriniMark: A Robust Generative Speech Watermarking Method for Trinity-Level Attribution

arXiv

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arXiv 2025年

作者： Li, Yue Liu, Weizhi Lin, Dongdong College of Computer Science and Technology National Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China

The emergence of diffusion models has facilitated the generation of speech with reinforced fidelity and naturalness. While deepfake detection technologies have manifested the ability to identify AI-generated content, their efficacy decreases as generative models become increasingly sophisticated. Furthermore, current research in the field has not adequately addressed the necessity for robust watermarking to safeguard the intellectual property rights associated with synthetic speech and generative models. To remedy this deficiency, we propose a robust generative speech watermarking method (TriniMark) for authenticating the generated content and safeguarding the copyrights by enabling the traceability of the diffusion model. We first design a structure-lightweight watermark encoder that embeds watermarks into the time-domain features of speech and reconstructs the waveform directly. A temporal-aware gated convolutional network is meticulously designed in the watermark decoder for bit-wise watermark recovery. Subsequently, the waveform-guided fine-tuning strategy is proposed for fine-tuning the diffusion model, which leverages the transferability of watermarks and enables the diffusion model to incorporate watermark knowledge effectively. When an attacker trains a surrogate model using the outputs of the target model, the embedded watermark can still be learned by the surrogate model and be correctly extracted. Comparative experiments with state-of-the-art methods demonstrate the superior robustness of our method, particularly in countering compound attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Audio watermarking

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SOLIDO: A Robust Watermarking Method for Speech Synthesis via Low-Rank Adaptation

arXiv

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arXiv 2025年

作者： Li, Yue Liu, Weizhi Lin, Dongdong College of Computer Science and Technology National Huaqiao University Xiamen361021 China The Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China

The accelerated advancement of speech generative models has given rise to security issues, including model infringement and unauthorized abuse of content. Although existing generative watermarking techniques have proposed corresponding solutions, most methods require substantial computational overhead and training costs. In addition, some methods have limitations in robustness when handling variable-length inputs. To tackle these challenges, we propose SOLIDO, a novel generative watermarking method that integrates parameter-efficient fine-tuning with speech watermarking through low-rank adaptation (LoRA) for speech diffusion models. Concretely, the watermark encoder converts the watermark to align with the input of diffusion models. To achieve precise watermark extraction from variable-length inputs, the watermark decoder based on depthwise separable convolution is designed for watermark recovery. To further enhance speech generation performance and watermark extraction capability, we propose a speech-driven lightweight fine-tuning strategy, which reduces computational overhead through LoRA. Comprehensive experiments demonstrate that the proposed method ensures high-fidelity watermarked speech even at a large capacity of 2000 bps. Furthermore, against common individual and compound speech attacks, our SOLIDO achieves a maximum average extraction accuracy of 99.20% and 98.43%, respectively. It surpasses other state-of-the-art methods by nearly 23% in resisting time-stretching attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech enhancement

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