School of Computer Science and Statistics
Multimodal Learning for DeepFake Detection In recent years, the rise of deepfakes—videos that are digitally altered using artificial intelligence (AI) to convincingly depict false events or individuals has sparked significant concerns about the reliability of visual content captured by surveillance systems. This project is driven by the recognition that traditional methods of detecting video manipulation … Read more
Automated surveillance is widely opted for applications such as traffic monitoring, vehicle identification, etc . But, various weather degradation factors such as rain and snow streaks, along with atmospheric veil severely affect the perceptual quality of an image, eventually affecting the performance of these applications. There exist weather specific (rain, haze, snow, etc .) methods … Read more
We will explore generative AI with a focus on 3D content creation and editing. You will build up your background in 3D computer graphics and 3D computer vision and conduct research in the following themes including neural radiance fields, diffusion models, or applied projects. 1. Neural radiance fields Project 1.1: Real-time neural radiance fields. We … Read more
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[MSc Level] This project will involve creating a real-time enfacment system from the state of the art deep-fake networks, and conducting perceptual experiments to identify if participants have ownership of their new face, and if properties of the source-actor (e.g., age, gender, etc.) can be perceived, even though the generated video is a photorealistic depiction … Read more
[MSc Level] We will collaborate with award-winning animation studio Cartoon Saloon to investigate animated character body part recognition. This project aims to search through cartoon scenes in order to identify character body parts. This task is more difficult than typical human pose-estimation as cartoon characters are often drawn to defy the laws of physics. Knowledge … Read more