Autonomous system operation requires real-time processing of measurement data which often contain significant
uncertainties and noise. Adversarial attacks have been widely studied to simulate these perturbations in recent years. To counteract
these attacks in autonomous systems, a novel defense method is proposed in this paper.
A stereo-regularizer is proposed to guide the
model to learn the implicit relationship between the left and right
images of the stereo-vision system. Univariate and multivariate
functions are adopted to characterize the relationships between the
two input images and the object detection model. The regularizer is
then relaxed to its upper bound to improve adversarial robustness.
Furthermore, the upper bound is approximated by the remainder
of its Taylor expansion to improve the local smoothness of the loss
surface. The model parameters are trained via adversarial training with the novel regularization term.
Our method exploits basic
knowledge from the physical world, i.e., the mutual constraints of
the two images in the stereo-based system. As such, outliers can be
detected and defended with high accuracy and efficiency. Numerical
experiments demonstrate that the proposed method offers superior
performance when compared with traditional adversarial training
methods in state-of-the-art stereo-based 3D object detection models
for autonomous vehicles.