3D Gaussian Splatting has advanced radiance field reconstruction, enabling high-quality view synthesis and fast rendering in 3D modeling. While adversarial attacks on object detection models are well-studied for 2D images, their impact on 3D models remains underexplored. This work introduces the Masked Iterative Fast Gradient Sign Method (M-IFGSM), designed to generate adversarial noise targeting the CLIP vision-language model. M-IFGSM specifically alters the object of interest by focusing perturbations on masked regions, degrading the performance of CLIP's zero-shot object detection capability when applied to 3D models. Using eight objects from the Common Objects 3D (CO3D) dataset, we demonstrate that our method effectively reduces the accuracy and confidence of the model, with adversarial noise being nearly imperceptible to human observers. The top-1 accuracy in original model renders drops from 95.4\% to 12.5\% for train images and from 91.2\% to 35.4\% for test images, with confidence levels reflecting this shift from true classification to misclassification, underscoring the risks of adversarial attacks on 3D models in applications such as autonomous driving, robotics, and surveillance. The significance of this research lies in its potential to expose vulnerabilities in modern 3D vision models, including radiance fields, prompting the development of more robust defenses and security measures in critical real-world applications.

本研究解决了对抗性攻击对3D建模中高质量视图合成与快速渲染影响的研究不足。提出了一种新的方法——掩蔽迭代快速梯度符号方法（M-IFGSM），通过对感兴趣物体的遮罩区域施加扰动，显著降低CLIP模型在3D模型上的零样本目标检测能力。实验表明，该方法明显减少了模型的准确性和信心，揭示了现代3D视觉模型的脆弱性，促使需要开发更强大的防御措施以保护关键应用。 

攻击下的高斯溅射：研究3D物体中的对抗噪声