Methods for detecting forged handwriting are usually based on the assumption that the forged handwriting is produced by humans. Authentic-looking handwriting, however, can also be produced synthetically. Diffusion-based generative models have recently gained popularity as they produce striking natural images and are also able to realistically mimic a person’s handwriting. It is, therefore, reasonable to assume that these models will be used to forge handwriting in the near future, adding a new layer to handwriting forgery detection. We show for the first time that the identification of synthetic handwritten data is possible by a small Convolutional Neural Network (ResNet18) reaching accuracies of 90%. We further investigate the existence of distinct discriminative features in synthetic handwriting data produced by latent diffusion models that could be exploited to build stronger detection methods. Our experiments indicate that the strongest discriminative features do not come from generation artifacts, letter shapes, or the generative model’s architecture, but instead originate from real-world artifacts in genuine handwriting that are not reproduced by generative methods.