  This paper presents a novel context-aware image denoising algorithm that
combines an adaptive image smoothing technique and color reduction techniques
to remove perturbation from adversarial images. Adaptive image smoothing is
achieved using auto-threshold canny edge detection to produce an accurate edge
map used to produce a blurred image that preserves more edge features. The
proposed algorithm then uses color reduction techniques to reconstruct the
image using only a few representative colors. Through this technique, the
algorithm can reduce the effects of adversarial perturbations on images. We
also discuss experimental data on classification accuracy. Our results showed
that the proposed approach reduces adversarial perturbation in adversarial
attacks and increases the robustness of the deep convolutional neural network
models.
