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Diffusion Models (DM) are highly effective at generating realistic,high-quality images. However, these models lack creativity and merely composeoutputs based on their training data, guided by a textual input provided atcreation 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 anartist's work then it was trained on some or all of his artworks thus violatingcopyright. In this paper, a preliminary study to infer the probability of useof an artist's name in the input string of a generated image is presented. Tothis aim we focused only on images generated by the famous DALL-E 2 andcollected images (both original and generated) of five renowned artists.Finally, a dedicated Siamese Neural Network was employed to have a first kindof probability. Experimental results demonstrate that our approach is anoptimal starting point and can be employed as a prior for predicting a completeinput string of an investigated image. Dataset and code are available at:https://github.com/ictlab-unict/not-with-my-name .

Not with my name! Inferring artists' names of input strings employed by Diffusion Models