IPA-NeRF: Illusory Poisoning Attack Against Neural Radiance Fields

Neural Radiance Field (NeRF) represents a significant advancement in computer vision, offering implicit neural network-based scene representation and novel view synthesis capabilities. Its applications span diverse fields including robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, etc., some of which are considered high-risk AI applications. However, despite its widespread adoption, the robustness and security of NeRF remain largely unexplored. In this study, we contribute to this area by introducing the Illusory Poisoning Attack against Neural Radiance Fields (IPA-NeRF). This attack involves embedding a hidden backdoor view into NeRF, allowing it to produce predetermined outputs, i.e. illusory, when presented with the specified backdoor view while maintaining normal performance with standard inputs. Our attack is specifically designed to deceive users or downstream models at a particular position while ensuring that any abnormalities in NeRF remain undetectable from other viewpoints. Experimental results demonstrate the effectiveness of our Illusory Poisoning Attack, successfully presenting the desired illusory on the specified viewpoint without impacting other views. Notably, we achieve this attack by introducing small perturbations solely to the training set. The code can be found at https://github.com/jiang-wenxiang/IPA-NeRF.

Photosequencing of Motion Blur using Short and Long Exposures

Photosequencing aims to transform a motion blurred image to a sequence of sharp images. This problem is challenging due to the inherent ambiguities in temporal ordering as well as the recovery of lost spatial textures due to blur. Adopting a computational photography approach, we propose to capture two short exposure images, along with the original blurred long exposure image to aid in the aforementioned challenges. Post-capture, we recover the sharp photosequence using a novel blur decomposition strategy that recursively splits the long exposure image into smaller exposure intervals. We validate the approach by capturing a variety of scenes with interesting motions using machine vision cameras programmed to capture short and long exposure sequences. Our experimental results show that the proposed method resolves both fast and fine motions better than prior works.