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 .

翻译：扩散模型（DM）在生成逼真、高质量图像方面极为有效。然而，这些模型缺乏创造力，仅根据其训练数据，结合创建时提供的文本输入来生成输出。使用艺术家的名字作为输入来生成让人联想到该艺术家的图像，这是否可接受？这意味着，如果DM能够复制某艺术家的作品，那么它一定是在该艺术家的部分或全部作品上训练过的，从而侵犯了版权。本文提出了一项初步研究，用于推断生成图像的输入字符串中使用艺术家名字的概率。为此，我们仅聚焦于著名的DALL-E 2生成的图像，并收集了五位著名艺术家的图像（包括原始作品和生成图像）。最后，采用了一种专门的孪生神经网络来获取初步的概率估计。实验结果表明，我们的方法是一个最优的起点，并可作为预测被研究图像完整输入字符串的先验条件。数据集和代码可在以下网址获取：https://github.com/ictlab-unict/not-with-my-name。