The 11th International Conference on Communication and Information Processing

2025年第十一届通信与信息处理国际会议

November 12-15, 2025 // Lingshui, Hainan, China

Chao Fang, Beijing University of Technology, China

Bio: Chao Fang received his B.S degree in Information Engineering from Wuhan University of Technology, Wuhan, China, in 2009, and the Ph.D. degree with the State Key Laboratory of Networking and Switching Technology in Information and Communication Engineering from Beijing University of Posts and Te4lecommunications, Beijing, China, in 2015. He joined the Beijing University of Technology in 2016 and now is an associate professor. From August 2013 to August 2014, he had been funded by China Scholarship Council to visit Carleton University, Ottawa, ON, Canada, as a joint doctorate. Moreover, he is the visiting scholars of University of Technology Sydney, Commonwealth Scientific and Industrial Research Organization, Hong Kong Polytechnic University, Kyoto University, Muroran Institute of Technology, and Queen Mary University of London.

Dr. Fang is the senior member of IEEE, and the vice chair of technical affairs committee in IEEE ComSoc Asia/Pacific Region (2022-2023). Moreover, he served as the Technical Program Committee Chair of SPCNC 2024, Workshop Chairs of ICFEICT (2022-2024) and ICNCIC (2023-2024), and Poster Co-Chair of HotICN 2018. He won the Best Paper Award of IEEE ICFEICT 2022 and 2024, ICCSN 2024, and NCIC 2024. His current research interests include future networks, intelligent cloud-edge-terminal cooperation computing, and intelligent network control.

Speech Title: Collaborative Allocation and Intelligent Optimization of Service-Driven Cloud Radio Access Network Resources

Abstract: In order to meet the service requirements of the emerging applications such as extended reality, 8K ultra-high definition video transmission and industrial Internet of Things in terms of massive user access, heterogeneous mobile traffic processing, ultra-low latency, ultra-high reliability and other aspects, cloud-edge collaboration, as the core of cloud radio access networks (C-RAN), has been increasingly concerned and risen to the height of national development strategy. At present, the problem on cooperative allocation and optimization of cloud-edge-end resources in C-RAN is still in the initial research stage, lacking systematic and in-depth research, which makes it difficult to adaptively guarantee the differentiated service requirements of network business. Therefore, by sorting out and referring to the research ideas and methods related to cloud computing and fog computing, and drawing on future network concepts such as "separation of control and forwarding" in software-defined networking and "in-network caching" in information-centric networking, the project focuses on collaborative allocation and intelligent optimization mechanisms of service-driven C-RAN resources from the perspective of cross-layer and cross-domain cooperation. To improve the overall service capacity and satisfy the differentiated service requirements of massive applications, key technologies such as multi-user-oriented cross-layer collaboration allocation and intelligent optimization of cloud-edge-terminal resources, multi-business-oriented cross-layer collaboration and intelligent resource allocation, multi-business-oriented cross-domain collaboration and intelligent resource allocation will be solved in C-RAN environments, providing customized service for network applications.

Gang Yang, Information Support Force Engineering University, China

Bio: Gang Yang is a cybersecurity researcher and a Committee Member of the Intelligent Evaluation Professional Committee at the Chinese Institute of Command and Control. He has served as a reviewer or PC member for prestigious conferences and journals, including TIP, ICASSP, and CogSci (all ranked CCF-A/B). His research portfolio includes leading 3 projects and contributing to 15 others, such as the National Key R&D Program of China. He has authored over 20 academic papers and holds 13 patents and software copyrights.

Speech Title: LLM-Empowered Security Evaluation for Information System Software

Abstrat: Modern information systems are evolving towards software-defined and intelligent paradigms, where the security of software systems themselves has become a critical safeguard for the entire information infrastructure. However, traditional software security testing typically require substantial manual efforts, making it challenging to keep pace with the expanding scale of software products. To this issue, we explore the application of Large Language Model(LLM) agent, and design workflows to uncover security vulnerabilities in third-party component libraries and Web interfaces. We map the human expert knowledge to specific testing phases within the agent's workflow and employing various control algorithms to control the testing actions to acquire an effective software testing. Experimental results demonstrate that our proposed solutions can effectively identify unknown vulnerabilities and, to a significant extent, substitute manual red teaming efforts, enabling more efficient and autonomous pentesting.

Yan Lin, Nanjing University of Science and Technology, China

BIO: Yan Lin received the M.S. and Ph.D. degree from Southeast University, China, in 2013 and 2018, respectively. She visited Southampton Wireless Group in Southampton University, U.K. from Oct. 2016 to Oct. 2017. She joined the Nanjing University of Science and Technology, China, in 2018, where she is currently an Associate Professor with the School of Electronic and Optical Engineering. Her current research interests include vehicular networks, UAV networks, mobile edge computing, and reinforcement learning for resource allocation in wireless communication. She has co-authored more than 40 journals and conferences, such as IEEE JSAC/TWC/TCOM/TVT/IOT/Network, and holds several Chinese patents. She has presided over and participated in several National Natural Science Foundation of China and other projects. She also has served as a TPC member and an invited speaker for several IEEE conferences, and as a reviewer for many IEEE journals and conferences.
Speech Title：Anti-Jamming Communication for UAV Swarms: Joint Resource Allocation and Trajectory Optimization Using Multi-Agent Reinforcement Learning
Abstract：The Unmanned Aerial Vehicles (UAVs) communication faces challenges arising from scarce spectrum resources and malicious jamming. This presentation introduces a novel spectrum-waterfall-assisted multi-agent anti-jamming framework for UAV swarms by designing joint resource allocation and trajectory optimization strategies. Based upon formulating the problem as a decentralized partially observable parameterized-action Markov Decision Process, a self-attention-based convolutional neural network is first employed to extract spatiotemporal spectrum-waterfall knowledge, and then a multi-agent hybrid Proximal Policy Optimization based anti-jamming scheme is proposed to maximize the long-term utility-cost trade-off. Simulation results show that the proposed scheme outperforms the benchmarks in terms of both the convergence and the long-term utility-cost trade-off, while achieving higher success rate with lower energy consumption with varying numbers of channels.

Ekbal Rashid, Professor in CSE (AI/ML) MRECW, Hyderabad

Bio: Dr. Ekbal Rashid is the Professor in the Department of Computer Science and Engineering (AI/ML) at Malla Reddy Engineering College for Women (MRECW) since October 2025 (Continue) Earlier he was also professor in the department of computer science and Engineering (AI/ML) in St. Peter’s Engineering College Hyderabad. He has completed Master of Computer Applications from Central University (IGNOU) , New Delhi in June 2003 and He has completed his M.Tech in Computer Science and Engineering from Birla Institute of Technology, MESRA in 2009 and Ph.D. in Computer Science and Engineering in May 2015 From Siksha O Anusandhan Deemed to be University Bhubaneswar, Odisha (Ranked Internationally by QS and THE World University Rankings 2023) and he also completed Post-Doc from Technical University of Sofia, Bulgaria in year 2024. He has supervised 2 Ph. D. Thesis (and one in pipeline) and 1 Master thesis in the area of Software Engineering, Machine Learning, Software Quality, Artificial Intelligence, Data Mining. Dr. Rashid has published around 65 research papers in international journals and conference proceedings of repute including Springer, IEEE, and Inderscience, Wseas.
Speech Title: The Forensic Examination and Recovery of Transient Messages in Android Social Media Applications
Abstract: This paper talks about requiring effective forensic recovery techniques for auto-deleted messages, such as Snapchat, WhatsApp, and Instagram, on the Android platform. Their temporality, coupled with encryption and deletion protocols, forms a great challenge for dilemmas in digital forensics and legal investigations. A specific specialised framework for Android forensics is then introduced to include rooting, memory extraction, and network analysis in the secured acquisition of transient messages legally. The paper assessed the effectiveness of the framework and highlighted the need for such techniques to improve the forensic capability so as to improve the balance between spending efforts to recover and preserve privacy.

Xiwen Zhang, Beijing Language and Culture University, China

Bio: XiWen Zhang is currently a full professor of Digital Media Department, School of Information Science, Beijing Language and Culture University. Prof. Zhang worked as an associated professor from 2002 to 2007 at the Human-computer interaction Laboratory, Institute of Software, Chinese Academy of Sciences. From 2005 to 2006 he was a Post doctor advised by Prof. Michael R. Lyu in the Department of Computer Science and Engineering, the Chinese University of Hong Kong. From 2000 to 2002 he was a Post doctor advised by Prof. ShiJie Cai in the Computer Science and Technology department, Nanjing University. Prof. Zhang's research interests include pattern recognition, computer vision, and human-computer interaction, as well as their applications in digital image, video, and ink. Prof. Zhang has published over 60 refereed journal and conference papers. His SCI papers are published in Pattern Recognition, IEEE Transactions on Systems Man and Cybernetics B, Computer-Aided Design. He has published more than twenty EI papers. Prof. Zhang received his B.E. in Chemical equipment and machinery from Fushun Petroleum Institute (became Liaoning Shihua University since 2002) in 1995, and his Ph.D. advised by Prof. ZongYing Ou in Mechanical manufacturing and automation from Dalian University of Technology in 2000.
Speech Title: Intelligently Recognizing and Generating Information from Digital Image
Abstract: Due to pattern recognition and deep learning, various information can be recognized and generated from image. Our work has focused on the proposed hierarchy models, local homogeneity, and adversarial generation.Various digital images are recognized, such as ones scanned from mechanical paper drawings and paper text, face images, portrait ones with line drawings, and microscopic bone marrow images. Various information is recognized using the proposed hierarchy models. Graphics and their multi-levels compounded objects are recognized from images scanned from mechanical paper drawings using a hierarchy model of engineering drawings. Faces and their components are recognized from photos using a facial model. Various information is recognized using the proposed local homogeneity. Karyocytes and their components from microscopic bone marrow images based on regional color features. Various information is generated from image using cycle-Consistent adversarial networks. Text is separated from grid background using cycle-Consistent adversarial networks. Digital images of Chinese classical upper-class lady paintings are generated from images with line drawings using conditional generative adversarial networks.

Hongmei Zhang, Guilin University of Electronic Technology, China

Bio: Dr. Zhang Hongmei is currently a professor at the college of Information and Communication Engineering, Guilin University of Electronic Technology. She received her Ph.D. degree in pattern recognition and intelligent system from East China University of Science and Technology in 2008. She was a visiting scholar of Cheng kung University, T.W. during Jul. 2010 to Oct. 2010 and Heriot Watt University, U.K. during Jul. 2014 to Jul. 2015. Her research areas include information system security, machine learning and embedded system. She has authored and co-authored more than 70 scientific paper and conference presentations.
Speech Title: Deepfake Image Detection: A Universal Approach via Vision-Language Models
Abstract: To address the issues of performance degradation and insufficient generalization ability of deepfake detection models when detecting fake images outside the training distribution, a general deepfake detection model (FEFA) based on vision-language models is proposed, aiming to leverage the zero-shot learning capability of vision-language models to improve forgery detection performance. The forgery-aware adapter (FAA) is integrated into the image encoder of the vision-language model to extract forgery features from both the image and frequency domains. In this work, a frequency band attention mechanism and a frequency detail enhancement component are further incorporated into the frequency domain branch of the FAA to enhance its capability in modeling frequency domain forgery characteristics. Experimental results show that the proposed method achieves an average accuracy of 94.75% across multiple public datasets, representing an improvement of 10.84% compared to current baseline model, thereby validating the model's cross-dataset generalization.