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Robust Watermarking Based on Multi-layer Watermark Feature Fusion

IEEE Transactions on Multimedia 2025年

作者： Wu, Shaowu Lu, Wei Luo, Xiangyang Sun Yat-Sen University School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan450002 China

The purpose of robust image watermarking is to embed a watermark into a carrier image in an invisible form and extract the watermark successfully even under noise interference conditions to achieve copyright confirmation and traceability. Although watermarking methods based on deep learning can improve the robustness by adding a noise simulation layer, few theoretical analyses of the codec structure have been conducted. Theoretical explainability is the theoretical basis for developing a network architecture, which plays a guiding role in network development. On the basis of the interpretability of convolutional networks, this paper analyzes the mathematical process of embedding and extracting watermarks in codecs and proposes a novel watermarking framework based on multi-layer watermark feature fusion. Specifically, the encoder can be a convolutional network structure of arbitrary depth, whereas the decoder needs only to adopt its corresponding deconvolution structure. To improve the quality and robustness of the generated watermarked image, the watermark is associated with an arbitrary layer feature space in the decoder. In the decoder, the network quickly converges to each original encoding feature space through the deconvolution structure, thus decoupling the watermark features. Finally, the watermark is extracted via the automatic fusion of multi-layer watermark features. The experimental results show that the proposed method is suitable for few-shot learning, and its invisibility, robustness and generalization performance on multiple datasets are significantly better than those of other advanced methods. © 2025 IEEE.

关键词： Decoding

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Adaptive focusing optical coherence tomography for corneal imaging

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Optics Letters 2025年第3期50卷 896-899页

作者： Shi, Yejiong He, Bin Yin, Zichen Chen, Zhengyu Hu, Zhangwei Xue, Ruizhi Yang, Panqi Zhang, Xiao Zhang, Ning Meng, Zhe Ying, Yuzhe Jing, Linkai Wang, Guihuai Xue, Ping State Key Laboratory of Low-dimensional Quantum Physics Department of Physics Tsinghua University Beijing China Frontier Science Center for Quantum Information Beijing China Beijing Institute of Technology Beijing China Institute of Forensic Science Ministry of Public Security Beijing China Department of Neurosurgery Beijing Tsinghua Changgung Hospital School of Clinical Medicine Institute of Precision Medicine Tsinghua University Beijing China

Optical coherence tomography (OCT) plays a crucial role in diagnosing corneal diseases due to its capacity to provide high-resolution three-dimensional imaging. However, the convex shape of the cornea and the inherent tradeoff between depth of field (DOF) and lateral resolution in OCT systems often result in defocusing issues, leading to reduced lateral resolution and sensitivity in single-shot high-resolution imaging. Traditional methods typically involve multiple focusing at different depths followed by image stitching to achieve full-depth high-resolution imaging of the cornea, which increases imaging times and introduces potential stitching artifacts. To address these limitations, we propose a novel adaptive focusing OCT system. By leveraging the symmetry of the corneal structure and the periodic focusing stability of an electrically tunable lens, our system can achieve full-depth high-resolution imaging of the entire cornea in a single scan, covering an 11 mm imaging range and 1.5 mm depth variation with a lateral resolution of 10 µm. This approach not only halves the imaging time but also eliminates the need for image stitching. Imaging of curved tape samples and excised porcine corneas demonstrates the potential of this novel technique for high-resolution and fast corneal imaging. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Optical coherence tomography

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Resource Allocation for MEC in Ultra-dense Networks

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Journal of Computers (Taiwan) 2025年第1期36卷 143-162页

作者： Ma, Huan Li, Shuyun Wang, Zhihui Dou, Xubing Zhang, Xinchang 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 Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China

In the complex context of 5G ultra-dense networks, the unprecedented growth in traffic and computing demand has made the interaction between edge devices and edge servers extremely intricate due to the time-varying nature of the network environment and resource heterogeneity. The combination of UDN and Mobile Edge Computing not only significantly improves network performance but also expands application scenarios and enhances user experience, marking it as one of the crucial trends in future network development. In this paper, we review recent results and research progress on MEC-based resource allocation in 5G ultra-dense networks. We analyze resource allocation strategies from multiple dimensions, including user association, interference management, energy efficiency optimization, delay reduction, access selection, and user mobility. Finally, we discuss the challenges and open issues that need to be addressed for future development in this area. © 2025 Codon Publications. All rights reserved.

关键词： 5G mobile communication systems

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Degree and Classification of Winding Deformation Faults Based on Least-Squares Fitting of Lissajous Trajectory Coefficient Recognition 3rd

Degree and Classification of Winding Deformation Faults Base...

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3rd International Conference on information Control, Electrical Engineering, and Rail Transit, ICEERT 2023

作者： Chen, Jianquan Zhaung, Yi Chen, Ruichuang Ma, Hongde Wu, Zhaoxiao Yunfu Power Supply Bureau of Guangdong Power Grid Co. Guangdong Yunfu527300 China Guangzhou Huali Science and Technology Vocational College Guangdong Guangzhou510000 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400000 China

ISBN: (纸本)9789819746194

The power transformer is the most commonly used equipment in the power system, and winding deformation is one of the most frequent transformer faults. The short-circuit impedance method based on ΔU–I to construct the Lissajous figures is a newly proposed winding deformation detection method in recent years, but there are still problems such as difficulty in detecting the small deformation of the winding, and it is difficult to distinguish between different types of faults. In this paper, on the basis of the existing ΔU–I constructing Lissajous figures, a winding deformation detection method based on the least-squares fitting Lissajous track coefficient identification is proposed, which can effectively detect the winding deformation and determine the degree and type of faults. Firstly, the primary side current and voltage difference are used to construct the Lissajous figures, and the lower half of the Lissajous trajectory is excised to change the trajectory into a single-valued function;then the least-squares method is used for fitting, and the identification of winding deformation is carried out based on the coefficients of the power series of the fitted coefficients. The research results show that the method can effectively identify the small deformation of the winding and its degree of deformation, and distinguish the amplitude deformation and axial deformation, which has a certain engineering value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Least squares approximations

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Realizing high-speed target tracking by using multi-rate feedforward predictive control for the acquisition, tracking, and pointing system

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Defence technology 2025年

作者： Li, Hang Peng, Gaoliang Shan, Xiaobiao Zhao, Mingyuan Zhang, Wei Wang, Jinghan Cheng, Feng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China College of Information Science and Technology Eastern Institute of Technology Ningbo315200 China

The acquisition, tracking, and pointing (ATP) system is widely used in target tracking, counter-UAV operations, and other related fields. As UAV technology develops, there is a growing demand to enhance the tracking capabilities of ATP systems. However, in practical applications, ATP systems face various design constraints and functional limitations, making it infeasible to indefinitely improve hardware performance to meet tracking requirements. As a result, tracking algorithms are required to execute increasingly complex tasks. This study introduces a multi-rate feedforward predictive controller to address issues such as low image feedback frequency and significant delays in ATP systems, which lead to tracking jitter, poor tracking performance, low precision, and target loss. At the same time, the proposed approach aims to improve the tracking capabilities of ATP systems for high-speed and highly maneuverable targets under conditions of low sampling feedback rates and high feedback delays. The method suggested is also characterized by its low order, fast response, and robustness to model parameter variations. In this study, an actual ATP system is built for target tracking test, and the proposed algorithm is fully validated in terms of simulation and actual system application verification. Results from both simulations and experiments demonstrate that the method effectively compensates for delays and low sampling rates. For targets with relative angular velocities ranging from 0 to 90°/s and angular accelerations between 0 and 470°/s2, the system improved tracking accuracy by 70.0%–89.9% at a sampling frequency of 50 Hz and a delay of 30 m s. Moreover, the compensation algorithm demonstrated consistent performance across actuators with varying characteristics, further confirming its robustness to model insensitivity. In summary, the proposed algorithm considerably enhances the tracking accuracy and capability of ATP systems for high-speed and highly maneuverable ta

关键词： Predictive control systems

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Enhancing Geolocation Accuracy of High-Altitude Airborne SAR Through Tropospheric Delay Compensation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xiang, Yaobing Sun, Yuli Lei, Lin Ji, Kefeng Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System College of Electronic Science and Technology Changsha410073 China

Geolocation is a crucial step in the processing of synthetic aperture radar (SAR) images. High-altitude airborne SAR systems present unique geolocation challenges due to travelling long distances through the troposphere. However, the impact of tropospheric delay on geolocation is often overlooked in existing airborne SAR studies, which can lead to inaccuracies. To address this issue, we propose a new positioning method, the tropospheric delay-compensated range-Doppler (TDC-RD) model. The TDC-RD model leverages reference atmospheric models to estimate and compensate for the tropospheric delay in SAR images. This model effectively mitigates the impact of tropospheric delay in SAR geolocation. To further optimize the TDC-RD model solution, a digital elevation model (DEM)-assisted dual iteration method is proposed. This method iteratively adjusts the target's plane position and elevation in an alternating manner. The effectiveness of the TDC-RD model has been validated through both simulation experiments and actual flight experiments. The results show a significant improvement in geolocation accuracy compared to existing methods, with a maximum reduction of 11.39 m and 24.33% in the mean absolute error (MAE) of SAR geolocation. The TDC-RD model has a great advantage in long-range SAR geolocation. Our research enhances the accuracy and stability of high-altitude airborne SAR geolocation without requiring ground control points. © 2025 IEEE.

关键词： Troposphere

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Query recovery from easy to hard: jigsaw attack against SSE 24

Query recovery from easy to hard: jigsaw attack against SSE

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Proceedings of the 33rd USENIX Conference on security Symposium

作者： Hao Nie Wei Wang Peng Xu Xianglong Zhang Laurence T. Yang Kaitai Liang Huazhong University of Science and Technology Huazhong University of Science and Technology and Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering and JinYinHu Laboratory and State Key Laboratory of Cryptology Huazhong University of Science and Technology and St. Francis Xavier University Delft University of Technology

ISBN: (纸本)9781939133441

Searchable symmetric encryption schemes often unintentionally disclose certain sensitive information, such as access, volume, and search patterns. Attackers can exploit such leakages and other available knowledge related to the user's database to recover queries. We find that the effectiveness of query recovery attacks depends on the volume/frequency distribution of keywords. Queries containing keywords with high volumes/frequencies are more susceptible to recovery, even when countermeasures are implemented. Attackers can also effectively leverage these "special" queries to recover all *** exploiting the above finding, we propose a Jigsaw attack that begins by accurately identifying and recovering those distinctive queries. Leveraging the volume, frequency, and cooccurrence information, our attack achieves 90% accuracy in three tested datasets, which is comparable to previous attacks (Oya et al., USENIX' 22 and Damie et al., USENIX' 21). With the same runtime, our attack demonstrates an advantage over the attack proposed by Oya et al (approximately 15% more accuracy when the keyword universe size is 15k). Furthermore, our proposed attack outperforms existing attacks against widely studied countermeasures, achieving roughly 60% and 85% accuracy against the padding and the obfuscation, respectively. In this context, with a large keyword universe (≥3k), it surpasses current state-of-the-art attacks by more than 20%.

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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

作者： Jinghui Ni Hui Cui Lihai Zhao Fengling Li Xiaohui Han Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Quan Cheng Laboratory Shandong Open University University of Technology Sydney

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA.

关键词： Training Data privacy Adaptation models Source coding Semantics Image retrieval Signal processing Protection Speech processing Knowledge transfer

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