Summary of Advirl: Reinforcement Learning-based Adversarial Attacks on 3d Nerf Models, by Tommy Nguyen and Mehmet Ergezer and Christian Green
AdvIRL: Reinforcement Learning-Based Adversarial Attacks on 3D NeRF Models
by Tommy Nguyen, Mehmet Ergezer, Christian Green
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Graphics (cs.GR); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract introduces AdvIRL, a novel framework for crafting adversarial Neural Radiance Fields (NeRF) models. This framework uses Instant Neural Graphics Primitives (Instant-NGP) and Reinforcement Learning to generate robust adversarial noise that remains effective under diverse 3D transformations. The approach is validated across various scenes, from small objects to large environments. Targeted attacks achieve high-confidence misclassifications, highlighting the practical risks posed by adversarial NeRFs. AdvIRL-generated models can also serve as training data to enhance the robustness of vision systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make 3D images that are tricky for computers to understand. This is called Neural Radiance Fields (NeRF), and it’s like taking a picture of an object from many different angles and then using those pictures to create a 3D model. But, just like how you can take a fake selfie to trick your friends, someone could create fake NeRF images that look real but are actually wrong. This paper shows how to make these fake images and use them to trick computers. It also shows how making these fake images can help computers become better at understanding what’s real and what’s not. |
Keywords
» Artificial intelligence » Reinforcement learning
