Quantitative Evaluation Based on CLIP for Methods Inhibiting Imitation of Painting Styles

Image generation AIs with the ability to generate a variety of high-quality images from text and images have been gaining attention, meanwhile, they cause a serious problem where a third party generates AI art that imitates the artist’s style by fine-tuning from a specific artist’s work. Against this background, a method has been proposed to add small noise perturbations to an artwork to inhibit imitation of the painting style. Currently, two such methods exist: Glaze and Mist, which apply perturbations to artworks so that a false style is learned, and Mist, which makes it difficult to extract features from the work. Glaze and Mist differ from each other in their evaluation manners, and they are not able to evaluate the real problem described above, i.e., the inhibition of imitating the style of a particular artist. Therefore, the purpose of this study is to realize a quantitative evaluation based on the index of artist-likeness. In this paper, we discuss three points: whether the proposed artist-likeness index and the correct prediction rate are suitable for quantitative evaluation of artist-likeness, how much it inhibits the imitation of painting style quantitatively, and whether Glaze or Mist is superior. Our experimental results confirm that the proposed metrics reflect the artist-likeness and quantitatively evaluate, how much Glaze and Mist inhibit the imitation of painting styles quantitatively, and we find that Mist is superior to Glaze according to our approach.