  The ability to detect whether an object is a 2D or 3D object is extremely
important in autonomous driving, since a detection error can have
life-threatening consequences, endangering the safety of the driver,
passengers, pedestrians, and others on the road. Methods proposed to
distinguish between 2 and 3D objects (e.g., liveness detection methods) are not
suitable for autonomous driving, because they are object dependent or do not
consider the constraints associated with autonomous driving (e.g., the need for
real-time decision-making while the vehicle is moving). In this paper, we
present EyeDAS, a novel few-shot learning-based method aimed at securing an
object detector (OD) against the threat posed by the stereoblindness syndrome
(i.e., the inability to distinguish between 2D and 3D objects). We evaluate
EyeDAS's real-time performance using 2,000 objects extracted from seven YouTube
video recordings of street views taken by a dash cam from the driver's seat
perspective. When applying EyeDAS to seven state-of-the-art ODs as a
countermeasure, EyeDAS was able to reduce the 2D misclassification rate from
71.42-100% to 2.4% with a 3D misclassification rate of 0% (TPR of 1.0). We also
show that EyeDAS outperforms the baseline method and achieves an AUC of over
0.999 and a TPR of 1.0 with an FPR of 0.024.
