  Autonomous vehicles (AV) have progressed rapidly with the advancements in
computer vision algorithms. The deep convolutional neural network as the main
contributor to this advancement has boosted the classification accuracy
dramatically. However, the discovery of adversarial examples reveals the
generalization gap between dataset and the real world. Furthermore, affine
transformations may also confuse computer vision based object detectors. The
degradation of the perception system is undesirable for safety critical systems
such as autonomous vehicles. In this paper, a deep learning system is proposed:
Affine Disentangled GAN (ADIS-GAN), which is robust against affine
transformations and adversarial attacks. It is demonstrated that conventional
data augmentation for affine transformation and adversarial attacks are
orthogonal, while ADIS-GAN can handle both attacks at the same time. Useful
information such as image rotation angle and scaling factor are also generated
in ADIS-GAN. On MNIST dataset, ADIS-GAN can achieve over 98 percent
classification accuracy within 30 degrees rotation, and over 90 percent
classification accuracy against FGSM and PGD adversarial attack.
