Papers › Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF Attack
Advancing Adversarial Robustness in GNeRFs: The IL2-NeRF Attack
Nicole Meng, Caleb Manicke, Ronak Sahu, Caiwen Ding, Yingjie Lao
Generalizable Neural Radiance Fields (GNeRF) are recognized as one of the most promising techniques for novel view synthesis and 3D model generation in real-world applications. However, like other generative models in computer vision, ensuring their adversarial robustness against various threat models is essential for practical use. The pioneering work in this area, NeRFool, introduced a state-of-the-art attack that targets GNeRFs by manipulating source views before feature extraction, successfully disrupting the color and density results of the constructed views. Building on this foundation, we propose IL2-NeRF (Iterative L2 NeRF Attack), a novel adversarial attack method that explores a new threat model (in the L2 domain) for attacking GNeRFs. We evaluated IL2-NeRF against two standard GNeRF models across three benchmark datasets, demonstrating similar performance compared to NeRFool, based on the same evaluation metrics proposed by NeRFool. Our results establish IL2-NeRF as the first adversarial method for GNeRFs under the L2 norm. We establish a foundational L2 threat model for future research, enabling direct performance comparisons while introducing a smoother, image-wide perturbation approach in Adversarial 3D Reconstruction. Our code is available at: https://github.com/The-NRC-SCAR-Group/IL2-NeRF
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections