Diffusion Models (DM) are highly effective at generating realistic, high-quality images. However, these models lack creativity and merely compose outputs based on their training data, guided by a textual input provided at creation time. Is it acceptable to generate images reminiscent of an artist, employing his name as input? This imply that if the DM is able to replicate an artist's work then it was trained on some or all of his artworks thus violating copyright. In this paper, a preliminary study to infer the probability of use of an artist's name in the input string of a generated image is presented. To this aim we focused only on images generated by the famous DALL-E 2 and collected images (both original and generated) of five renowned artists. Finally, a dedicated Siamese Neural Network was employed to have a first kind of probability. Experimental results demonstrate that our approach is an optimal starting point and can be employed as a prior for predicting a complete input string of an investigated image. Dataset and code are available at: https://github.com/ictlab-unict/not-with-my-name .

该论文旨在研究通过艺术家的姓名作为输入生成类似其作品的图像，并使用一种专用的孪生神经网络推断生成图像中是否含有艺术家姓名的概率。实验结果表明，这种方法是一个理想的起点，并且可以作为预测调查图像的完整输入字符串的先验信息。

不用我的名字！推测由扩散模型使用的输入字符串的艺术家姓名