AdvSplat：对抗攻击针对前馈高斯点云模型
AdvSplat: Adversarial Attacks on Feed-Forward Gaussian Splatting Models
AI总结：
本文研究了对抗攻击对前馈高斯点云模型的影响，提出两种高效的黑盒算法，通过频域参数化优化像素空间扰动，验证了输入图像中不可察觉的扰动对重建结果的破坏性。
摘要：
3D高斯点云（3DGS）正被越来越多地认可为实时、高保真3D重建的强大范式。然而，其每场景优化流程限制了可扩展性和泛化能力，并阻碍了高效的推理。最近出现的前馈3DGS模型通过大规模预训练后，能够从少量输入视角快速重建，而无需特定场景优化。尽管其优势和商业部署潜力显著，但神经网络作为核心也放大了对抗操纵的风险。本文提出AdvSplat，首次系统研究对抗攻击对前馈3DGS的影响。我们首先利用白盒攻击揭示该模型家族的基本漏洞。然后开发了两种改进的、实际相关且查询高效的黑盒算法，通过频域参数化优化像素空间扰动：一种基于梯度估计，另一种无梯度，无需访问模型内部。在多个数据集上的广泛实验表明，AdvSplat可通过在输入图像中注入不可察觉的扰动显著破坏重建结果。我们的发现揭示了一个被忽视但紧迫的问题，希望引起社区对这一新兴安全和鲁棒性挑战的关注。
英文摘要：
3D Gaussian Splatting (3DGS) is increasingly recognized as a powerful paradigm for real-time, high-fidelity 3D reconstruction. However, its per-scene optimization pipeline limits scalability and generalization, and prevents efficient inference. Recently emerged feed-forward 3DGS models address these limitations by enabling fast reconstruction from a few input views after large-scale pretraining, without scene-specific optimization. Despite their advantages and strong potential for commercial deployment, the use of neural networks as the backbone also amplifies the risk of adversarial manipulation. In this paper, we introduce AdvSplat, the first systematic study of adversarial attacks on feed-forward 3DGS. We first employ white-box attacks to reveal fundamental vulnerabilities of this model family. We then develop two improved, practically relevant, query-efficient black-box algorithms that optimize pixel-space perturbations via a frequency-domain parameterization: one based on gradient estimation and the other gradient-free, without requiring any access to model internals. Extensive experiments across multiple datasets demonstrate that AdvSplat can significantly disrupt reconstruction results by injecting imperceptible perturbations into the input images. Our findings surface an overlooked yet urgent problem in this domain, and we hope to draw the community's attention to this emerging security and robustness challenge.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
