论文标题
对抗性颜色增强：通过优化颜色过滤器生成不受限制的对抗图像
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter
论文作者
论文摘要
我们介绍了一种方法，该方法可以使用颜色过滤器来增强图像，以创建对抗性效应，从而使神经网络陷入错误分类。我们的方法，对抗性颜色增强（ACE），通过通过梯度下降优化颜色过滤器来生成不受限制的对抗图像。 ACE的新颖性是它以透明的方式纳入了图像增强的既定实践。实验结果验证了ACE的白盒对抗强度和黑盒可传递性。一系列示例展示了ACE产生的图像的感知质量。 ACE对最近的工作做出了重要的贡献，该工作超出了$ L_P $的不可识别，并专注于无限制的对抗性修改，这些修改产生了大量可感知的扰动，但对人的眼睛仍然不舒服。还在两个方向上探索了基于滤波器的对手的未来潜力：具有通用增强实践（例如Instagram过滤器）的指导ACE对特定有吸引力的图像样式，并将ACE适应图像语义。代码可在https://github.com/zhengyuzhao/ace上找到。
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.
