With the maturity of depth sensors, point clouds have received increasing attention in various applications such as autonomous driving, robotics, surveillance, etc., while deep point cloud learning models have shown to be vulnerable to adversarial attacks. Existing attack methods generally add/delete points or perform point-wise perturbation over point clouds to generate adversarial examples in the data space, which may neglect the geometric characteristics of point clouds. Instead, we propose point cloud attacks from a new perspective -- Graph Spectral Domain Attack (GSDA), aiming to perturb transform coefficients in the graph spectral domain that corresponds to varying certain geometric structure. In particular, we naturally represent a point cloud over a graph, and adaptively transform the coordinates of points into the graph spectral domain via graph Fourier transform (GFT) for compact representation. We then analyze the influence of different spectral bands on the geometric structure of the point cloud, based on which we propose to perturb the GFT coefficients in a learnable manner guided by an energy constraint loss function. Finally, the adversarial point cloud is generated by transforming the perturbed spectral representation back to the data domain via the inverse GFT (IGFT). Experimental results demonstrate the effectiveness of the proposed GSDA in terms of both imperceptibility and attack success rates under a variety of defense strategies. The code is available at https://github.com/WoodwindHu/GSDA.
翻译:随着深度传感器的成熟,点云在诸如自主驾驶、机器人、监视等各种应用中日益受到越来越多的注意,而深点云学习模型则表明很容易受到对抗性攻击的攻击。现有的攻击方法通常在点云上添加/显示点点点,或者对点云进行点对点扰动,以便在数据空间中产生对抗性的例子,这可能忽视点云的几何特点。相反,我们从一个新的角度提出点云攻击 -- -- 光谱攻击图(GSDA),目的是干扰图形光谱域中与某些几何结构相适应的系数的转换。特别是,我们自然代表一个图上的一个点云,并且以适应的方式将点的坐标通过图示 Fourier变换(GFT)将点的坐标转换成图表光谱域。然后我们分析不同频谱带对点云的几何结构的影响,在此基础上,我们提议在能量限制损失功能的指导下,以可学习的方式破坏GFT系数。最后,通过将GFDA/SDA的可变射频度值转换到G格式,从而显示GFTA/FTA值的可追溯到GFDFDFDA值。