  In this paper, detection of deception attack on deep neural network (DNN)
based image classification in autonomous and cyber-physical systems is
considered. Several studies have shown the vulnerability of DNN to malicious
deception attacks. In such attacks, some or all pixel values of an image are
modified by an external attacker, so that the change is almost invisible to the
human eye but significant enough for a DNN-based classifier to misclassify it.
This paper first proposes a novel pre-processing technique that facilitates the
detection of such modified images under any DNN-based image classifier as well
as the attacker model. The proposed pre-processing algorithm involves a certain
combination of principal component analysis (PCA)-based decomposition of the
image, and random perturbation based detection to reduce computational
complexity. Next, an adaptive version of this algorithm is proposed where a
random number of perturbations are chosen adaptively using a doubly-threshold
policy, and the threshold values are learnt via stochastic approximation in
order to minimize the expected number of perturbations subject to constraints
on the false alarm and missed detection probabilities. Numerical experiments
show that the proposed detection scheme outperforms a competing algorithm while
achieving reasonably low computational complexity.
