  Compared with transferable untargeted attacks, transferable targeted
adversarial attacks could specify the misclassification categories of
adversarial samples, posing a greater threat to security-critical tasks. In the
meanwhile, 3D adversarial samples, due to their potential of multi-view
robustness, can more comprehensively identify weaknesses in existing deep
learning systems, possessing great application value. However, the field of
transferable targeted 3D adversarial attacks remains vacant. The goal of this
work is to develop a more effective technique that could generate transferable
targeted 3D adversarial examples, filling the gap in this field. To achieve
this goal, we design a novel framework named TT3D that could rapidly
reconstruct from few multi-view images into Transferable Targeted 3D textured
meshes. While existing mesh-based texture optimization methods compute
gradients in the high-dimensional mesh space and easily fall into local optima,
leading to unsatisfactory transferability and distinct distortions, TT3D
innovatively performs dual optimization towards both feature grid and
Multi-layer Perceptron (MLP) parameters in the grid-based NeRF space, which
significantly enhances black-box transferability while enjoying naturalness.
Experimental results show that TT3D not only exhibits superior cross-model
transferability but also maintains considerable adaptability across different
renders and vision tasks. More importantly, we produce 3D adversarial examples
with 3D printing techniques in the real world and verify their robust
performance under various scenarios.
