A Differentiable Color Filter for Generating Unrestricted Adversarial Images
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We propose Adversarial Color Filtering (AdvCF), an approach that uses a differentiable color filter to create adversarial images. The color filter allows us to introduce large perturbations into images, while still maintaining or enhancing their photographic quality and appeal. AdvCF is motivated by properties that are necessary if adversarial images are to be used to protect the content of images shared online from unethical machine learning classifiers: First, perturbations must be imperceptible and adversarial images must look realistic to the human eye. Second, adversarial impact must be maintained in the face of classifiers unknown when the perturbations are generated (transferability). The paper presents evidence that AdvCF has these two properties, and also points out that AdvCF has the potential for further improvement if image semantics are taken into account.
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