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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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VoWi-Fi security threats: Address resolution protocol attack and countermeasures

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IET Networks 2024年第2期13卷 129-146页

作者： Lu, Kuan-Chu Liu, I.-Hsien Chang, Keng-Hao Li, Jung-Shian Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan

B5G/6G networks are facing challenges in the deployment of additional base stations. However, Taiwan's four major operators have launched VoWi-Fi calling services to maintain signal quality and coverage for customers. These services pose potential threats when users connect to untrusted Wi-Fi networks. Therefore, the authors utilised commercial equipment to study the security of VoWi-Fi calling services offered by Taiwan's four major telecom companies. The authors employed address resolution protocol attack methods to develop two verification attacks that bypass existing security measures: one for dropping session initiation protocol packets and the other for dropping voice call packets, both capable of circumventing current security defences. Through real-world experiments, the authors confirmed their feasibility and assessed their potential harm. Consequently, two defence methods are proposed. The first is an anti-attack algorithm for app and device manufacturers to detect the security of the user's calling environment. The second is a recommendation for telecom operators to implement new detection mechanisms to safeguard user rights. The cover image is based on the Case Study VoWi-Fi security threats: Address resolution protocol attack and countermeasures by Kuan-Chu Lu et al., https://***/10.1049/ntw2.12113. © 2024 The Authors. IET Networks published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Wi-Fi

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Common criteria for security evaluation and malicious intrusion detection mechanism of dam supervisory control and data acquisition system

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IET Networks 2024年第5-6期13卷 546-559页

作者： Lu, Kuan-Chu Liu, I-Hsien Liu, Zong-Chao Li, Jung-Shian Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan

Supervisory control and data acquisition (SCADA) systems are vital in monitoring and controlling industrial processes through the web. However, while such systems result in lower costs, greater utilisation efficiency, and improved reliability, they are vulnerable to cyberattacks, with consequences ranging from the inconvenience and minor disruption to severe physical damage and even loss of life. The authors evaluate the security of the Dam system in the form of Common Criteria, develop safety goals to improve this safety, and focus on threats and risks to the dam SCADA system. Finally proposes an anomaly-based machine-learning framework for detecting malicious network attacks in the SCADA system of a dam. Three unsupervised classification algorithms are considered: hierarchical clustering, local outlier factor, and isolation forest. It is shown that the hierarchical clustering algorithm achieves the highest precision and F-score of the three algorithms. Overall, the results confirm the effectiveness of anomaly-based detection algorithms in enhancing the robustness of SCADA systems toward malicious attacks. At the same time, it complies with the security objectives of Common Criteria, achieving the safety and protection of the dam. © 2024 The Author(s). IET Networks published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Internet of things

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Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Adversarial attacks and defenses for digital communication signals identification

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Digital communications and Networks 2024年第3期10卷 756-764页

作者： Qiao Tian Sicheng Zhang Shiwen Mao Yun Lin College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China College of Information and Communication Engineering Harbin Engineering UniversityHarbin150000China Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849USA

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management *** has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and ***,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial *** examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong ***,the security of AI models for the digital communication signals identification is the premise of its efficient and credible *** this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial *** we present more detailed adversarial indicators to evaluate attack and defense ***,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

关键词： Digital communication signals identification AI model Adversarial attacks Adversarial defenses Adversarial indicators

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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PriChain: Efficient Privacy-Preserving Fine-Grained Redactable Blockchains in Decentralized Settings

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Chinese Journal of Electronics 2025年第1期34卷 82-97页

作者： Hongchen Guo Weilin Gan Mingyang Zhao Chuan Zhang Tong Wu Liehuang Zhu Jingfeng Xue School of Computer Science and Technology Beijing Institute of Technology School of Cyberspace Science and Technology Beijing Institute of Technology School of Computer and Communication Engineering University of Science and Technology Beijing

Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.

关键词： Resistance Access control Data privacy Dictionaries Blockchains Encryption Complexity theory Usability Protection

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Sentiment analysis algorithm based on word embedding in text mining

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International Journal of Speech Technology 2025年第1期28卷 141-151页

作者： Hu, Qiong School of Computer and Communication Engineering Nanjing Tech University Pujiang Institute Nanjing210000 China

This study proposes a sentiment analysis algorithm model that combines word embedding and machine learning algorithms to address issues such as data shortage, low accuracy, and limited coverage. The proposed model uses the continuous bag-of-words word embedding method for word analysis and incorporates convolutional neural network, long and short-term memory neural network model, and attention mechanism to enhance the traditional word embedding *** research results obtained using the English review dataset and the hotel review dataset, which contain an average of 10,000 entries, show that in the performance comparison of hotel review sentiment analysis tests, the highest value of the model loss function used in this study is 0.92. The accuracy, recall, and F1 value of the model are 90.56%, 91.65%, and 92.36%, respectively, which are 5.89%, 7.00%, and 6.74% higher than the lowest values in the convolutional neural network, long short term memory neural network, and convolutional long short term memory neural network. In the performance comparison for different emotional tendencies, the algorithm model used in this study achieved various indicator values of 94.68%, 93.86%, and 94.68% in sentiment analysis, respectively. The model with attention mechanism has a higher accuracy, with an accuracy of 96.4%. This shows that the algorithmic model used in the study is able to improve the accuracy and effectiveness of sentiment analysis to some extent. The research model is particularly suitable for sentiment analysis tasks in scenarios such as product reviews and hotel evaluations on e-commerce platforms, and can extract key emotional information from massive user reviews. This provides strong support for user sentiment analysis and data-driven decision-making in industries such as e-commerce and tourism. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Accuracy Attention mechanisms Convolutional neural networks Long short term memory neural network Sentiment analysis Text mining

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Preamble slice orderly queue access scheme in cell-free dense communication systems

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Digital communications and Networks 2025年第1期11卷 126-135页

作者： Jun Sun Mengzhu Guo Jian Liu School of Telecommunications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Computer and Communication Engineering University of Science and TechnologyBeijing 100083China

High reliability applications in dense access scenarios have become one of the main goals of 6G *** solve the access collision of dense Machine Type communication(MTC)devices in cell-free communication systems,an intelligent cooperative secure access scheme based on multi-agent reinforcement learning and federated learning is proposed,that is,the Preamble Slice Orderly Queue Access(PSOQA)*** this scheme,the preamble arrangement is combined with the access *** preamble arrangement is realized by preamble slices which is from the virtual preamble *** access devices learn to queue orderly by deep reinforcement *** orderly queue weakens the random and avoids collision.A preamble slice is assigned to an orderly access queue at each access *** orderly queue is determined by interaction information among multiple *** the federated reinforcement learning framework,the PSOQA scheme is implemented to guarantee the privacy and security of ***,the access performance of PSOQA is compared with other random contention schemes in different load *** results show that PSOQA can not only improve the access success rate but also guarantee low-latency tolerant performances.

关键词： Machine type communication Random access Reinforcement learning Access success probability Federated learning

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An Energy Distribution Correlation Judgment Method for Interrupted Sampling Repeater Jamming Suppression

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Progress In Electromagnetics Research B 2024年 105卷 59-78页

作者： Li, Ji Su, Fan Wang, Wei Yan, Rui Li, Jialiang College of Computer and Communication Engineering Changsha University of Science & Technology Changsha410000 China

Interrupted Sampling Repeater Jamming (ISRJ) can produce several false targets through intermittent sampling and forwarding of the intercepted signals. The paper proposes an interference identification and suppression method based on Short-Time Fourier Transform-Energy Distribution Correlation Judgment (STFT-EDCJ) to lessen the impact of the false targets mixed in echo pulses. Firstly, the method obtains the energy distribution of echoes in the time-frequency domain employing the short-time Fourier transform, extracts the time slice of higher energy targets through energy peak detection, and then calculates the Pearson correlation coefficient (PCC) of the energy distribution in the frequency domain of each target time slice to construct the Target PCC Datasets (TPCCD). Secondly, it distinguishes between the real target and false targets after echo pulse pressure by the range and specificity of TPCCD. Finally, it uses mapping the time domain position of the false targets to suppress interference. The abundant simulation results verify the proposed method’s effectiveness, and the Monte Carlo simulation demonstrates the method’s effectiveness under ISRJ models. © (2023), (Electromagnetics Academy). All Rights Reserved.

关键词： Monte Carlo methods

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