  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. A range of
examples demonstrates the perceptual quality of images that ACE produces. ACE
makes an important contribution to recent work that moves beyond $L_p$
imperceptibility and focuses on unrestricted adversarial modifications that
yield large perceptible perturbations, but remain non-suspicious, to the human
eye. The future potential of filter-based adversaries is also explored in two
directions: guiding ACE with common enhancement practices (e.g., Instagram
filters) towards specific attractive image styles and adapting ACE to image
semantics. Code is available at https://github.com/ZhengyuZhao/ACE.
