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arXiv 2025年

作者： Zhang, Mingkun Bi, Keping Chen, Wei Guo, Jiafeng Cheng, Xueqi State Key Lab of AI Safety CAS Key Lab of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

In this paper, we aim to build an adversarially robust zero-shot image classifier. We ground our work on CLIP, a vision-language pre-trained encoder model that can perform zero-shot classification by matching an image with text prompts "a photo of a .". Purification is the path we choose since it does not require adversarial training on specific attack types and thus can cope with any foreseen attacks. We then formulate purification risk as the KL divergence between the joint distributions of the purification process of denoising the adversarial samples and the attack process of adding perturbations to benign samples, through bidirectional Stochastic Differential Equations (SDEs). The final derived results inspire us to explore purification in the multi-modal latent space of CLIP. We propose two variants for our CLIPure approach: CLIPure-Diff which models the likelihood of images’ latent vectors with the DiffusionPrior module in DaLLE-2 (modeling the generation process of CLIP’s latent vectors), and CLIPure-Cos which models the likelihood with the cosine similarity between the embeddings of an image and "a photo of a.". As far as we know, CLIPure is the first purification method in multi-modal latent space and CLIPure-Cos is the first purification method that is not based on generative models, which substantially improves defense efficiency. We conducted extensive experiments on CIFAR-10, ImageNet, and 13 datasets that previous CLIP-based defense methods used for evaluating zero-shot classification robustness. Results show that CLIPure boosts the SOTA robustness by a large margin, e.g., from 71.7% to 91.1% on CIFAR10, from 59.6% to 72.6% on ImageNet, and 108% relative improvements of average robustness on the 13 datasets over previous SOTA. The code is available at https://***/TMLResearchGroup-CAS/CLIPure. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System 21

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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21st Annual IEEE International Conference on Sensing, Communication, and networking, SECON 2024

作者： Kong, Lingchang Wang, Xinyu Zhou, Hao Zhou, Fengyu Jiang, Shenyao Zhu, Peide Song, Qi Liu, Zhi LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer Science City University of Hong Kong Hong Kong School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186% while reducing the mechanical rotations by 83.3%. © 2024 IEEE.

关键词： Inductive power transmission

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Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance 25

Unbiased Learning to Rank with Query-Level Click Propensity ...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Lulu Yu Keping Bi Jiafeng Guo Shihao Liu Dawei Yin Xueqi Cheng CAS Key Lab of Network Data Science and Technology ICT Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China

ISBN: (纸本)9798400713316

Most existing unbiased learning-to-rank (ULTR) approaches are based on the user examination hypothesis, which assumes that users will click a result only if it is both relevant and observed (typically modeled by position). However, in real-world scenarios, users often click only one or two results after examining multiple relevant options, due to limited patience or because their information needs have already been satisfied. Motivated by this, we propose a query-level click propensity model to capture the probability that users will click on different result lists, allowing for non-zero probabilities that users may not click on an observed relevant result. We hypothesize that this propensity increases when more potentially relevant results are present, and refer to this user behavior as relevance saturation bias. Our method introduces a Dual Inverse Propensity Weighting (DualIPW) mechanism-combining query-level and position-level IPW-to address both relevance saturation and position bias. Through theoretical derivation, we prove that DualIPW can learn an unbiased ranking model. Experiments on the real-world Baidu-ULTR dataset demonstrate that our approach significantly outperforms state-of-the-art ULTR baselines. The code can be found at https://***/Trustworthy-Information-Access/DualIPW.

关键词： position bias

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Distributed Deep Learning for Cooperative Computation Offloading in Low Earth Orbit Satellite networks

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China Communications 2022年第4期19卷 230-243页

作者： Qingqing Tang Zesong Fei Bin Li School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China

Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

关键词： LEO satellite networks computation offloading deep neural networks

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FLAP: Federated Learning Aggregation Scheme Based on Privileged Secret Sharing

FLAP: Federated Learning Aggregation Scheme Based on Privile...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Mu, Xianyu Tian, Youliang Xiong, Jinbo Wang, Shuai Gong, Boxiang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Guizhou Xishan Technology Co. Ltd China

ISBN: (纸本)9798350327380

Federated Learning (FL) is a privacy-preserving machine learning technique that trains models on client devices and only uploads new model gradients to servers for aggregation. However, transmitting the true gradients introduces the risk of reverse inference attack, which can compromise client privacy. To address this issue, we present the federated learning aggregation scheme based on privileged secret sharing (FLAP). Firstly, we present privileged secret sharing by combining the one-time pad with shamir secret sharing. Secondly, we introduce game theory to generate games between servers to resist collusion attacks. Finally, the analysis demonstrates that FLAP enables the server to successfully update the global gradient while ensuring the utmost protection of client privacy and FLAP achiev the effect of resisting collusion attack and reverse inference attack. We have proved through experiments that FLAP is 26.42% more efficient than the current scheme, and has strong robustness to drop out. © 2023 IEEE.

关键词： Game theory

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MEFold: Memory-Efficient Optimization for Protein Language Models via Chunk and Quantization

MEFold: Memory-Efficient Optimization for Protein Language M...

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International Joint Conference on Neural networks (IJCNN)

作者： Yanfeng Jiang Ning Sun Zhengxian Lu Shuang Peng Yi Zhang Fei Yang Tao Li College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Zhejiang Lab Hangzhou China Haihe Lab of ITAI Tianjin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Protein language models are currently experiencing a surge in demand owing to their remarkable accuracy in protein structure prediction. Nevertheless, their applications are hindered by the significant computation and memory requirements. The existing optimization strategies primarily focus on computational efficiency while often neglecting memory optimization, thereby restricting their suitability for devices with limited resources. In this paper, we propose MEFold, a novel memory-efficient optimization framework for protein language models that enables efficient inference on resource-constrained devices. MEFold consists of Look-up Table Chunk and Fine-grained Quantization. Look-up Table Chunk reduces the memory of intermediate activations by chunk and avoids the overhead of obtaining the optimal chunk size configuration through pre-computing. For the memory of model parameters, Fine-grained Quantization, delicately controls the scope of quantization to ensure that memory reduction is achieved while preventing declines in accuracy and computational speed. Experimental results show that, compared to the original model, for protein sequences ranging from 74 to 1024 in length, our method significantly reduces the peak memory during inference from 14.7-54.2GB to 6.0-14.4GB, while minimizing the impact on inference latency. On CASP14 and CAMEO datasets, the accuracy loss compared to the original model is below 1%. Moreover, our optimization provides various memory-saving alternatives. Our code is available at https://***/llwx593/MEFold.

关键词： Proteins Quantization (signal) Accuracy Computational modeling Memory management Neural networks Predictive models

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Community discovery method based on complex network of data fusion based on the super network perspective

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International Journal of Computers and Applications 2021年第4期43卷 383-390页

作者： Pei, Li School of Computer Science & Technology Xi‘an University of Posts and Telecommunications Xi‘an China Shaanxi Key Laboratory of Network Data Intelligent Processing Xi‘an University of Posts and Telecommunications Xi‘an China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi‘an Jiaotong University Xi‘an China

To enhance the computational efficiency and precision of community discovery, a community discovery algorithm with the mixed label based on the minimum description length (MDI) of information compression is proposed in this paper. Firstly, the community detection is converted to the information compression problem of seeking for an effective network structure and the quality evaluation function is constructed based on the MDL criterion. Secondly, the community discovery algorithm with heuristic mixed label movement is constructed based on the label node movement algorithm and Louvain community addition algorithm so as to reduce the quality evaluation function. Finally, the simulation experiment in the standard test set and API capture Sina microblog dataset shows that the proposed algorithm is superior to the selected comparison algorithm in computational efficiency and precision. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Computational efficiency

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CoCGAN: Contrastive Learning for Adversarial Category Text Generation 29

CoCGAN: Contrastive Learning for Adversarial Category Text G...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Sheng, Xin Xu, Linli Xu, Yinlong Bao, Changcun Chen, Huang Ren, Bo Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent Youtu Lab China

The task of generating texts of different categories has attracted more and more attention in the area of natural language generation recently. Meanwhile, generative adversarial net (GAN) has demonstrated its effectiveness on text generation, and is further applied to category text generation in later works. Different from existing methods, which mainly consider the pairwise relations between the text embedding and the corresponding fixed one-hot class label (data-to-class relations), this paper proposes a novel Contrastive Category Generative Adversarial Net (CoCGAN) to incorporate contrastive learning into adversarial category text generation, considering more flexible data-to-class relations as well as relations between the multiple text embeddings in the same batch (data-to-data relations). The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples. Accordingly, the generator tries to produce more realistic samples which can confuse the discriminator. Experimental results on both synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generation models. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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Spatial-Temporal Reconstruction Error for AIGC-based Forgery Image Detection

Spatial-Temporal Reconstruction Error for AIGC-based Forgery...

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

作者： Chengji Shen Zhenjiang Liu Kaixuan Chen Jie Lei Mingli Song Zunlei Feng State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Idlefish Network Technology Alibaba Group College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusion reconstruction error has emerged, as images generated by diffusion are more easily reconstructed by any diffusion model. However, we find that existing methods only use reconstruction error from a single time step, failing to fully leverage the entire reconstruction process. To this end, we propose to comprehensively consider every single time step to form the Temporal Reconstruction Error (TRE) that offers a richer feature representation. Furthermore, we design temporal aggregation and spatial focusing modules from two dimensions respectively to more effectively extract discriminative information from the TRE feature. Finally, we validate the proposed method on two popular datasets, and experimental results demonstrate that the proposed approach achieves state-of-the-art performance.

关键词： Deepfakes Image synthesis Focusing Signal processing Feature extraction Diffusion models Forgery data mining Speech processing Image reconstruction

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Look Globally and Reason: Two-stage Path Reasoning over Sparse Knowledge Graphs

arXiv

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arXiv 2024年

作者： Guan, Saiping Wei, Jiyao Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Sparse Knowledge Graphs (KGs), frequently encountered in realworld applications, contain fewer facts in the form of (head entity, relation, tail entity) compared to more populated KGs. The sparse KG completion task, which reasons answers for given queries in the form of (head entity, relation, ?) for sparse KGs, is particularly challenging due to the necessity of reasoning missing facts based on limited facts. Path-based models, known for excellent explainability, are often employed for this task. However, existing path-based models typically rely on external models to fill in missing facts and subsequently perform path reasoning. This approach introduces unexplainable factors or necessitates meticulous rule design. In light of this, this paper proposes an alternative approach by looking inward instead of seeking external assistance. We introduce a two-stage path reasoning model called LoGRe (Look Globally and Reason) over sparse KGs. LoGRe constructs a relation-path reasoning schema by globally analyzing the training data to alleviate the sparseness problem. Based on this schema, LoGRe then aggregates paths to reason out answers. Experimental results on five benchmark sparse KG datasets demonstrate the effectiveness of the proposed LoGRe model. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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