Artwork Protection Against Neural Style Transfer Using Locally Adaptive
  Adversarial Color Attack
Zhongliang Guo Kaixuan Wang Weiye Li Yifei Qian Ognjen Arandjelović Lei Fang
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.