Yu et al. proposed an attribution network architecture to map an input image to its corresponding fingerprint image. Therefore, they learned a model fingerprint for each source, such that the correlation index between one image fingerprint and each model fingerprint serves as softmax logit for classification. Their proposed approach was tested using real faces from CelebA database and synthetic faces created through different GAN approaches, achieving a final 99.5% fake detection accuracy for the best performance. However, this approach seemed not to be very robust against unseen simple image perturbation attacks such as noise, blur, cropping or compression, unless the models were re-trained again.