  Previous work has shown that 3D point cloud classifiers can be vulnerable to
adversarial examples. However, most of the existing methods are aimed at
white-box attacks, where the parameters and other information of the
classifiers are known in the attack, which is unrealistic for real-world
applications. In order to improve the attack performance of the black-box
classifiers, the research community generally uses the transfer-based black-box
attack. However, the transferability of current 3D attacks is still relatively
low. To this end, this paper proposes Scale and Shear (SS) Attack to generate
3D adversarial examples with strong transferability. Specifically, we randomly
scale or shear the input point cloud, so that the attack will not overfit the
white-box model, thereby improving the transferability of the attack. Extensive
experiments show that the SS attack proposed in this paper can be seamlessly
combined with the existing state-of-the-art (SOTA) 3D point cloud attack
methods to form more powerful attack methods, and the SS attack improves the
transferability over 3.6 times compare to the baseline. Moreover, while
substantially outperforming the baseline methods, the SS attack achieves SOTA
transferability under various defenses. Our code will be available online at
https://github.com/cuge1995/SS-attack
