On behalf of the Organizing Committee, it is our immense pleasure and honor to invite you to the 8th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2025), Shanghai, October 15-18, 2025.
PRCV is the largest and most comprehensive technical conference in China, focusing on pattern recognition, computer vision, and their applications, is listed in the CCF-C category. It offers a comprehensive technical program presenting all the latest developments in research and technology in the industry that attracts thousands of professionals annually. PRCV 2025 is co-organized by the China Society of Image and Graphics (CSIG), the Chinese Association for Artificial Intelligence (CAAI), the China Computer Federation (CCF), and the Chinese Association of Automation (CAA), and hosted by Shanghai Jiao Tong University.
PRCV 2025 will include 4 keynote speeches from academicians and worldwide leaders, as well as 8 invited talks from nationally recognized talents and IEEE Fellows, offering forefront and profound academic insights. In addition, over ten creative companies will engage in the conference, enabling deep integration of academia, industry, and research, accelerating further development in pattern recognition and computer vision.
The University of Western Australia (UWA)
Josef Kittler, former President of the International Association for Pattern Recognition, Fellow of the Royal Academy of Engineering, Distinguished Professor at the University of Surrey, IAPR Fellow, IEEE/IET Fellow
Presentation title:
Digital Content forensics in the context of large models
Speech abstract:
In the digital era, with the rapid development of artificial intelligence technology, especially the wide application of deep learning technology, the generation and editing of digital content has become more convenient and efficient. However, the double-edged nature of technology also brings new challenges in the field of digital content forensics. Generative large models, which can generate realistic text, images, audio and video, are likely to be widely used for malicious purposes such as false information and deep forgery, posing a threat to social order and information security. In the context of large models, forensics work becomes more complex and requires a higher level of technical means to cope with the continuous progress of counterfeiting technology.
Xiong Hongkai, Distinguished Professor of Shanghai Jiao Tong University, Cheung Kong Scholar of the Ministry of Education, National Jieqing, leading talent of Ten thousand People Program, Deputy director of the "Visual Big Data" special Committee of the Chinese Society of Image and Graphics, and member of the Chinese Society of Electronics
Presentation title:
Digital Content forensics in the context of large models
Speech abstract:
In the digital era, with the rapid development of artificial intelligence technology, especially the wide application of deep learning technology, the generation and editing of digital content has become more convenient and efficient. However, the double-edged nature of technology also brings new challenges in the field of digital content forensics. Generative large models, which can generate realistic text, images, audio and video, are likely to be widely used for malicious purposes such as false information and deep forgery, posing a threat to social order and information security. In the context of large models, forensics work becomes more complex and requires a higher level of technical means to cope with the continuous progress of counterfeiting technology.
Yang Jian, Professor of Nanjing University of Science and Technology, National Jieqing, Deputy director of Pattern Recognition Special Committee of Artificial Intelligence Society, director of Pattern Recognition Special Committee of Jiangsu Artificial Intelligence Society, IAPR Fellow, national leading talent
Presentation title:
Digital Content forensics in the context of large models
Speech abstract:
In the digital era, with the rapid development of artificial intelligence technology, especially the wide application of deep learning technology, the generation and editing of digital content has become more convenient and efficient. However, the double-edged nature of technology also brings new challenges in the field of digital content forensics. Generative large models, which can generate realistic text, images, audio and video, are likely to be widely used for malicious purposes such as false information and deep forgery, posing a threat to soc
