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

• URL: http://arxiv.org/abs/2101.05833v1
• Date: Thu, 14 Jan 2021 19:15:28 GMT
• Title: Context-Aware Image Denoising with Auto-Threshold Canny Edge Detection to Suppress Adversarial Perturbation
• Authors: Li-Yun Wang, Yeganeh Jalalpour, Wu-chi Feng
• Abstract summary: This paper presents a novel context-aware image denoising algorithm. It combines an adaptive image smoothing technique and color reduction techniques to remove perturbation from adversarial images. Our results show that the proposed approach reduces adversarial perturbation in adversarial attacks and increases the robustness of the deep convolutional neural network models.
• Score: 0.8021197489470756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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