RemedyGS：防御针对3D高斯泼溅的计算成本攻击
RemedyGS: Defend 3D Gaussian Splatting Against Computation Cost Attacks
针对3DGS重建流程的计算成本攻击，提出「检测器+净化器」的黑盒防御范式，并用对抗训练把净化结果拉回自然图像分布；做3DGS安全部署或需要清洗被投毒输入图像的研究者可直接复用该检测-净化两阶段结构。
原文依据 we propose the first effective and comprehensive black-box defense framework, named RemedyGS, against such computation cost attacks
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论作为三维重建的主流技术，3D高斯泼溅（3DGS）已被应用于广泛的应用与服务中。近期研究揭示了该流程中的关键脆弱性，并提出了计算成本攻击，可导致恶意资源占用甚至拒绝服务（DoS）状态，从而阻碍3DGS的可靠部署。本文提出首个有效且全面的黑盒防御框架RemedyGS，用于抵御此类计算成本攻击，保障3DGS重建系统与服务的安全。该流程包含两个关键组件：一个检测器，用于识别带有投毒纹理的被攻击输入图像；以及一个净化器，用于从被攻击图像中恢复良性图像，从而减轻这些攻击的不利影响。此外，我们将对抗训练引入净化器，以强制恢复图像与原始自然图像之间的分布对齐，从而提升防御效果。实验结果表明，该框架能够有效防御3DGS系统中的白盒、黑盒与自适应攻击，在安全性与可用性两方面均达到最优性能。代码已发布于 https://github.com/Polly-LYP/RemedyGS。
英文原文摘要 1298 字符
As a mainstream technique for 3D reconstruction, 3D Gaussian splatting (3DGS) has been applied in a wide range of applications and services. Recent studies have revealed critical vulnerabilities in this pipeline and introduced computation cost attacks that lead to malicious resource occupancies and even denial-of-service (DoS) conditions, thereby hindering the reliable deployment of 3DGS. In this paper, we propose the first effective and comprehensive black-box defense framework, named RemedyGS, against such computation cost attacks, safeguarding 3DGS reconstruction systems and services. Our pipeline comprises two key components: a detector to identify the attacked input images with poisoned textures and a purifier to recover the benign images from their attacked counterparts, mitigating the adverse effects of these attacks. Moreover, we incorporate adversarial training into the purifier to enforce distributional alignment between the recovered and original natural images, thereby enhancing the defense efficacy. Experimental results demonstrate that our framework effectively defends against white-box, black-box, and adaptive attacks in 3DGS systems, achieving state-of-the-art performance in both safety and utility. Our code is available at https://github.com/Polly-LYP/RemedyGS.