M-IFGSM, a masked iterative FGSM attack on CLIP, reduces top-1 accuracy to 12.5% on training renders and 35.4% on test renders of 3D Gaussian Splatting models.
2024 Li , Yanjie ; Xie , Bin ; Guo , Songtao ; Yang , Yuanyuan ; Xiao , Bin: A survey of robustness and safety of 2d and 3d deep learning models against adversarial attacks
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Gaussian Splatting Under Attack: Investigating Adversarial Noise in 3D Objects
M-IFGSM, a masked iterative FGSM attack on CLIP, reduces top-1 accuracy to 12.5% on training renders and 35.4% on test renders of 3D Gaussian Splatting models.