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This paper presents a novel context-aware image denoising algorithm thatcombines an adaptive image smoothing technique and color reduction techniquesto remove perturbation from adversarial images. Adaptive image smoothing isachieved using auto-threshold canny edge detection to produce an accurate edgemap used to produce a blurred image that preserves more edge features. Theproposed algorithm then uses color reduction techniques to reconstruct theimage using only a few representative colors. Through this technique, thealgorithm can reduce the effects of adversarial perturbations on images. Wealso discuss experimental data on classification accuracy. Our results showedthat the proposed approach reduces adversarial perturbation in adversarialattacks and increases the robustness of the deep convolutional neural networkmodels.

Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation