2020

Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter

Zhao, Zhengyu, Liu, Zhuoran, Larson, Martha

Understand

We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification.

Our approach, Adversarial Color Enhancement (ACE), generates unrestricted adversarial images by optimizing the color filter via gradient descent.
The novelty of ACE is its incorporation of established practice for image enhancement in a transparent manner.
Experimental results validate the white-box adversarial strength and black-box transferability of ACE.

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