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IEEE International Conference on Big Data

作者： Wenshan Zhang Xi Zhang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665480468

Propaganda information that aims at influencing people’s opinions is becoming more and more common around people, especially through social media. For the detection of propaganda content, researchers have conducted studies from multiple perspectives, including document level, sentence level and fragment level. However, most existing methods have mainly focused on propaganda detection in a single language because of the lack of multilingual datasets. In this article, we first propose a Chinese and English propaganda detection dataset. Then we propose a cross-lingual propaganda detection method, using semantic and syntactic information between different languages. Extensive experiments on our multilingual dataset demonstrate the effectiveness of our proposal in cross-lingual propaganda detection.

关键词： Social networking (online) Fuses Semantics Syntactics Big Data Transformers Information filters

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An Efficient Active Learning based Method for Deepfake Vidoes Model Attribution

An Efficient Active Learning based Method for Deepfake Vidoe...

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Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Xiaotian Si Weiqiang Jiang Linghui Li Xiaoyong Li Kaiguo Yuan Zhongyuan Guo Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications Beijing China Chine Mobile Group Co. Ltd Beijing China

To enhance the forensic investigation of deepfake face-swap videos, it is essential to attribute the specific generation model used to create these videos. Despite the remarkable progress made in data-driven approaches, recent algorithms continue to encounter challenges in learning from limited annotated data, thereby limiting their performance. In this paper, we present a novel active learning framework for the model attribution of deepfake videos. Specifically, our approach leverages active learning to select the most informative videos, thereby effectively reducing the annotation effort. To achieve this, we propose a query function based on clustering, where the clustering features are generated by a projection network trained through supervised contrastive learning. Extensive experiments demonstrate that our method achieves state-of-the-art performance in model attribution of deepfake videos, surpassing other baseline methods in the field of active learning.

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Introducing Natural Language-based Instruction Protocol for Intelligent Machine Collaborations

Introducing Natural Language-based Instruction Protocol for ...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Haobing Gong Hui Gao Yuting Feng Caixia Yuany Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

The rapid development of various autonomous unmanned systems has increased the endogenous intelligence of machines, which empowers functional collaborations among intelligent machines. However, traditional protocols for intelligent machine collaboration have limitations regarding functionality, efficiency, and scalability. Moreover, existing research on intelligent machine collaboration is not yet approaching a unified paradigm that facilitates interactions among machines and between machines and humans. Therefore, aiming to enable a more efficient functional collaboration among intelligent machines, we propose a natural language-based instruction (NLI) protocol, which enjoys the advantages of autonomy, robustness and efficiency. In particular, we specify the NLI protocol architecture by introducing a corpus of NLIs, an NLI generation module, and an NLI parsing module, wherein the corpus contains control instructions, intents and slots, the generation module is used to generate NLIs, and the parsing module is used to parse the intents and slots in the instruction. Furthermore, we case-study the proposed NLI protocol in an electromagnetic interference avoidance scenario based on semi-physical simulation with software-defined radio. Simulation results show that the NLI protocol is more robust and efficient than traditional control protocol in severe wireless channels, which validates the feasibility and effectiveness of implementing intelligent machine collaboration based on NLI.

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Trade less Accuracy for Fairness and Trade-off Explanation for GNN

Trade less Accuracy for Fairness and Trade-off Explanation f...

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IEEE International Conference on Big Data

作者： Yazheng Liu Xi Zhang Sihong Xie Key Laboratory of Trustworthy Distributed Computing and Service (MoE) BUPT Computer Science and Engineering Dept Lehigh University

ISBN: (纸本)9781665480468

Graphs are widely found in social network analysis and e-commerce, where Graph Neural Networks (GNNs) are the state-of the-art model. GNNs can be biased due to sensitive attributes and network topology. With existing work that learns a fair node representation or adjacency matrix, achieving a strong guarantee of group fairness while preserving prediction accuracy is still challenging, with the fairness-accuracy trade-off remaining obscure to human decision-makers. We first define and analyze a novel upper bound of group fairness to optimize the adjacency matrix for fairness without significantly h arming prediction accuracy. To understand the nuance of fairness-accuracy tradeoff, we further propose macroscopic and microscopic explanation methods to reveal the trade-offs and the space that one can exploit. The macroscopic explanation method is based on stratified sampling and linear programming to deterministically explain the dynamics of the group fairness and prediction accuracy. Driving down to the microscopic level, we propose a path-based explanation that reveals how network topology leads to the tradeoff. On seven graph datasets, we demonstrate the novel upper bound can achieve more efficient fairness-accuracy trade-offs and the intuitiveness of the explanation methods can clearly pinpoint where the trade-off is improved.

关键词： Training Upper bound Network topology Social networking (online) Microscopy Big Data Linear programming

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A Fusion Scheme of Region of Interest Extraction in Incomplete Fingerprint

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Chinese Journal of Electronics 2023年第4期21卷 663-666页

作者： Xiaojun JING Bo ZHANG Jie ZHANG Mingliang ZHONG School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

A fusion scheme is proposed for incomplete fingerprint to extract the Region of interest (ROI) centered at the reference point. Firstly, the orientation entropy is computed. Secondly, it is necessary for ROI extraction to segment foreground and background efficiently. So, the feature vector, based on orientation and gray, is defined for segmentation with SVM. Then, the fingerprint segmentation of the incomplete area is re-computed and measured with correlation and competition of texture, which is based on Local binary pattern (LBP). Finally, the proposed method is based on mutual information of fingerprint orientation and Poincare Index to detect the reference point of fingerprints, and then extract ROI. The performance of the new method is evaluated on FVC2004 database. And the performance is shown in the experiments and proves that it could locate the position of reference point of all type fingerprints more effectively and precisely.

关键词： Support vector machines Image segmentation Smoothing methods Fingerprint recognition Feature extraction Entropy Robustness

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BiTrust: Hybrid Trust Management for Secure Data Transmission in LEO Satellite Networks

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IEEE Internet of Things Journal 2025年

作者： Wei, Xinghai Yuan, Jie Hu, Runshan Wang, Haiguang Liu, Xiang Liu, Dongxiao Wang, Xingwu Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Huawei Technologies Co. Ltd Beijing100095 China Huawei International Shield Lab Changi Business Park 486035 Singapore Pengcheng Laboratory Shenzhen518083 China

Due to characteristics such as the openness and exposure of inter-satellite links, Low-Earth Orbit (LEO) satellite networks are subject to heightened vulnerability to malicious attacks compared to ground-based networks. Given various security risks, implementing trust management in LEO satellite networks becomes imperative. However, existing trust management schemes tailored for this scenario are vulnerable to a spectrum of attacks, resulting in low detection rates and poor network performance. To address the above issues, we propose BiTrust, a hybrid trust management scheme for secure data transmission in LEO satellite networks. BiTrust introduces two distinct types of trust: state trust and behavior trust. State trust leverages remote attestation technology to verify the authenticity of node identities and the integrity of their functions, ensuring that nodes consistently disseminate reliable behavior trust announcements to safeguard the trust plane. Behavior trust, on the other hand, utilizes a trust model to quantify the real time data forwarding behavior characteristics of nodes, thereby maintaining the reliability of the data plane. To precisely quantify behavior trust, we design a trust model based on multi-path trust propagation. By integrating an unstable penalty term, the proposed trust model can effectively defend against dynamic dropping misbehavior. Besides, to facilitate the deployment of BiTrust in a distributed manner, we introduce a two-hop rely message mechanism and a query-answer mechanism to support the construction of behavior trust. Experimental results confirm the efficacy of BiTrust, demonstrating a significant improvement of up to 64.3% in data transmission rate under highly untrusted environments while maintaining affordable overhead. © 2014 IEEE.

关键词： Risk management

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RDI: An adversarial robustness evaluation metric for deep neural networks based on sample clustering features

arXiv

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arXiv 2025年

作者： Song, Jialei Zuo, Xingquan Wang, Feiyang Huang, Hai Zhang, Tianle Shool of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China Shool of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

Deep neural networks (DNNs) are highly susceptible to adversarial samples, raising concerns about their reliability in safety-critical tasks. Currently, methods of evaluating adversarial robustness are primarily categorized into attack-based and certified robustness evaluation approaches. The former not only relies on specific attack algorithms but also is highly time-consuming, while the latter due to its analytical nature, is typically difficult to implement for large and complex models. A few studies evaluate model robustness based on the model’s decision boundary, but they suffer from low evaluation accuracy. To address the aforementioned issues, we propose a novel adversarial robustness evaluation metric, Robustness Difference Index (RDI), which is based on sample clustering features. RDI draws inspiration from clustering evaluation by analyzing the intra-class and inter-class distances of feature vectors separated by the decision boundary to quantify model robustness. It is attack-independent and has high computational efficiency. Experiments show that, RDI demonstrates a stronger correlation with the gold-standard adversarial robustness metric of attack success rate (ASR). The average computation time of RDI is only 1/30 of the evaluation method based on the PGD attack. Our open-source code is available at: https://***/r/RDI-B1DA. Copyright © 2025, The Authors. All rights reserved.

关键词： Open systems

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TKCA:a timely keystroke-based continuous user authentication with short keystroke sequence in uncontrolled settings

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Cybersecurity 2021年第1期4卷 177-192页

作者： Lulu Yang Chen Li Ruibang You Bibo Tu Linghui Li Institute of Information Engineering Chinese Academy of Sciences100093 BeijingChina School of Cyber Security University of Chinese Academy of Sciences100049 BeijingChina Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and Telecommunications100876 BeijingChina

keystroke-based behavioral biometrics have been proven effective for continuous user *** state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some *** remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled *** this work,we propose a Timely keystroke-based method for Continuous user Authentication,named *** integrates the key name and two kinds of timing features through an embedding *** it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory(Bi-LSTM)*** conduct a series of experiments to validate it on a public dataset-the Clarkson II dataset collected in a completely uncontrolled and natural *** results show that the proposed TKCA achieves state-of-the-art performance with 8.28%of EER when using only 30 keystrokes and 2.78%of EER when using 190 keystrokes.

关键词： keystroke dynamics Continuous user authentication Embedding LSTM Bi-LSTM

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A Non-Statistical Reinstatement Algorithm for Orientation Field of Incomplete Fingerprint

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Chinese Journal of Electronics 2023年第3期21卷 177-180页

作者： Xiaojun JING Bo ZHANG Xinjing LIU Dong WANG School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

Fingerprint orientation is the key information in fingerprint enhancement and matching. According to the problem of incomplete fingerprint orientation, a novel algorithm of fingerprint orientation reinstatement is proposed based on Local binary pattern (LBP) and mutual information function. Firstly, mutual information of fingerprint orientation is computed. Secondly, the feature vector is defined for incomplete area classification by Support vector machines (SVM). Then, the fingerprint orientation field of the incomplete area is re-computed and measured by LBP and mutual information function. For orientation reinstatement, LBP is used to measure the similarity of incomplete and complete area, while the mutual information function is used to measure correlation and competition of the neighborhood. Additionally, fuzzy criterion is proposed to assign different weights for neighborhood block and incomplete block for modifying orientation. As a result, the modified fingerprint orientation is used for post-processing. And the performance is shown in the experimental and proves the efficiency and reliability of our algorithm.

关键词： Support vector machines Correlation Area measurement Coherence Fingerprint recognition Entropy Classification algorithms

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Incorporate Personas and Emotions into Dialog Generation

Incorporate Personas and Emotions into Dialog Generation

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Science and Technology Innovation (IAECST), International Academic Exchange Conference on

作者： Yufeng Wu Chengyu Liu Wenjian Guo Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Since the Turing test, creating believable open-domain dialogues has been a basic problem in the study of artificial intelligence. Achieving success with dialogue models can greatly improve natural human-computer interactions and open up new possibilities for applications such as chatbots for therapy. Incorporating emotional information into dialogue systems and giving them personas can enhance user satisfaction by fostering more natural communication and human-like conversations. Both of these study directions are comparatively well-established, with important accomplishments in them. On the other hand, various roles in real-life discussion scenarios portray emotions in different ways. In order to create more fulfilling and human-like discussions, we investigate in this study the integration of personas and emotions in dialogue creation systems. Through the combination of explicit and implicit persons, we tackle the problem of individualized and emotional conversation. While implicit personas are gleaned from linguistic style, colloquial idioms, and personality attributes, explicit personas are derived from user profiles. Explicit personas and emotional labels are incorporated into the dialogue agent, and implicit personas are captured through the use of latent variables in a pre-trained Transformer model. Our suggested method enhances the authenticity of discussions and user experience by producing responses that are consistent with personas and emotions, hence improving the quality of communication.

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