MVT-Attack: Momentum Variance Tuning for Transferable Point Cloud Attacks

The vulnerability of 3D deep learning models has garnered significant attention.However, existing point cloud adversarial attacks exhibit limited transferability due to insufficient modeling of gradient dynamics. Instantaneous gradients provide limited characterization of local optimization uncertainty, while cross-iteration gradient evolution remains insufficiently exploited, leading to unstable optimization trajectories. To address this issue, we propose MVT-Attack (Momentum Variance Tuning Attack), a novel framework that enhances attack transferability through a dual gradient regulation mechanism. Unlike previous approaches that rely on complex loss-function regularization, MVT-Attack directly regulates gradient dynamics through a two-stage optimization strategy. First, we introduce a variance-guided gradient rectification mechanism that estimates local gradient fluctuations via normal-constrained sampling and adaptively adjusts gradients to reduce source-model-specific optimization bias. Subsequently, we design a momentum optimization module that aggregates historically refined gradients to construct smoother and more consistent update directions. The proposed framework demonstrates the effectiveness of gradient dynamics regulation to improve attack transferability and achieves superior performance on the ModelNet40 and ScanObjectNN benchmarks, consistently outperforming state-of-the-art methods.