Neural style transfer (NST) is widely adopted in computer vision to generate new images with arbitrary styles. This process leverages neural networks to merge aesthetic elements of a style image with the structural aspects of a content image into a harmoniously integrated visual result. However, unauthorized NST can exploit artwork. Such misuse raises socio-technical concerns regarding artists' rights and motivates the development of technical approaches for the proactive protection of original creations. Adversarial attack is a concept primarily explored in machine learning security. Our work introduces this technique to protect artists' intellectual property. In this paper Locally Adaptive Adversarial Color Attack (LAACA), a method for altering images in a manner imperceptible to the human eyes but disruptive to NST. Specifically, we design perturbations targeting image areas rich in high-frequency content, generated by disrupting intermediate features. Our experiments and user study confirm that by attacking NST using the proposed method results in visually worse neural style transfer, thus making it an effective solution for visual artwork protection.

使用本文提出的局部自适应对抗色彩攻击（LAACA）方法，通过攻击神经风格迁移技术（NST），可以对图像进行不可察觉的扰动，从而保护视觉艺术品的原创性。

使用局部自适应对抗色彩攻击保护艺术品免受神经风格迁移的影响