CAGE-GS: Low-Resolution Prior-Guided Gaussian Expansion Control Against Computation Cost Attacks on 3D Gaussian Splatting

Computational cost attacks against 3D Gaussian Splatting (3DGS) can substantially increase the memory and training overhead of 3DGS optimization, potentially causing denial-of-service (DoS). Existing defenses mainly rely on image purification or frequency filtering. However, because adversarial perturbations overlap with legitimate high-frequency scene details, aggressive input processing can suppress valid textures while leaving abnormal Gaussian geometry and spatial congestion insufficiently constrained. Motivated by the reduced effectiveness of such attacks at low resolution, we propose CAGE-GS, a coarse-to-fine defense that combines mild wavelet denoising with low-resolution reconstruction to derive structural and density priors. During full-resolution optimization, these priors reject structurally implausible Gaussian candidates and suppress excessive local expansion. Extensive experiments across three benchmark datasets show that CAGE-GS restores computational overhead toward clean-training levels while preserving reconstruction fidelity, and achieves better clean-input reconstruction quality than existing defense methods.