With the increasing attention in various 3D safety-critical applications, point cloud learning models have been shown to be vulnerable to adversarial attacks. Although existing 3D attack methods achieve high success rates, they delve into the data space with point-wise perturbation, which may neglect the geometric characteristics. Instead, we propose point cloud attacks from a new perspective -- the graph spectral domain attack, aiming to perturb graph transform coefficients in the spectral domain that corresponds to varying certain geometric structure. Specifically, leveraging on graph signal processing, we first adaptively transform the coordinates of points onto the spectral domain via graph Fourier transform (GFT) for compact representation. Then, we analyze the influence of different spectral bands on the geometric structure, based on which we propose to perturb the GFT coefficients via a learnable graph spectral filter. Considering the low-frequency components mainly contribute to the rough shape of the 3D object, we further introduce a low-frequency constraint to limit perturbations within imperceptible high-frequency components. Finally, the adversarial point cloud is generated by transforming the perturbed spectral representation back to the data domain via the inverse GFT. Experimental results demonstrate the effectiveness of the proposed attack in terms of both the imperceptibility and attack success rates.
翻译:随着各种3D安全关键应用的日益关注,点云学习模型被证明容易受到对抗性攻击。虽然现有的3D攻击方法取得了高成功率,但它们以点向的扰动进入数据空间,这可能忽略几何特征。相反,我们提议从一个新的角度来点云攻击,即图形光谱域攻击,目的是干扰图形改变与某些几何结构相对应的光谱域系数。具体地,利用图形信号处理,我们首先通过Fourier变换图(GFT)将点坐标适应性地转换到光谱域,以便进行压缩。然后,我们分析不同频谱带对几何结构的影响,据此我们提议通过可学习的图形光谱过滤器来扰动GFT系数。考虑到低频部分主要有助于3D对象的粗糙形状,我们进一步引入低频限制,以限制不易感知的高频度部件内扰动。最后,通过将攻击率的每平位光谱图光谱代表率转换为攻击率,并通过Gversefer 显示攻击率的实验性结果。