  Despite the high quality performance of the deep neural network in real-world
applications, they are susceptible to minor perturbations of adversarial
attacks. This is mostly undetectable to human vision. The impact of such
attacks has become extremely detrimental in autonomous vehicles with real-time
"safety" concerns. The black-box adversarial attacks cause drastic
misclassification in critical scene elements such as road signs and traffic
lights leading the autonomous vehicle to crash into other vehicles or
pedestrians. In this paper, we propose a novel query-based attack method called
Modified Simple black-box attack (M-SimBA) to overcome the use of a white-box
source in transfer based attack method. Also, the issue of late convergence in
a Simple black-box attack (SimBA) is addressed by minimizing the loss of the
most confused class which is the incorrect class predicted by the model with
the highest probability, instead of trying to maximize the loss of the correct
class. We evaluate the performance of the proposed approach to the German
Traffic Sign Recognition Benchmark (GTSRB) dataset. We show that the proposed
model outperforms the existing models like Transfer-based projected gradient
descent (T-PGD), SimBA in terms of convergence time, flattening the
distribution of confused class probability, and producing adversarial samples
with least confidence on the true class.
