High Quality-of-Experience Real-time Video for Smart Online Shopping. This project aims to develop high quality-of-experience real-time video systems for smart shopping applications by devising new deep-neural-network-enhanced video delivery schemes. It will generate new knowledge of combined AI and network solutions to achieve high-quality and low-latency real-time video delivery, addressing unsatisfactory user experience intrinsically caused by network delay and bandwidth. Fundamental principl ....High Quality-of-Experience Real-time Video for Smart Online Shopping. This project aims to develop high quality-of-experience real-time video systems for smart shopping applications by devising new deep-neural-network-enhanced video delivery schemes. It will generate new knowledge of combined AI and network solutions to achieve high-quality and low-latency real-time video delivery, addressing unsatisfactory user experience intrinsically caused by network delay and bandwidth. Fundamental principles and an all-in-one platform will be developed to address research problems and the industrial partner’s practical problems. It will significantly benefit all shopping businesses and their customers in Australia, as well as all other video-related services (e.g., online education, video conferencing, etc.).Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100477
Funder
Australian Research Council
Funding Amount
$421,554.00
Summary
Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and ....Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and protect privacy in AI applications. The anticipated outcomes should provide significant benefits to a wide range of applications, such as providing timely alerts to the media, government organisations, and the industry about misleading fake visual data, and preventing financial crimes on synthetic identity fraud.Read moreRead less