Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis

In the field of image forensics, notable attention has been recently paid toward the detection of synthetic contents created through Generative Adversarial Networks (GANs), especially face images. This work explores a classification methodology inspired by the inner architecture of typical GANs, where vectors in a low-dimensional latent space are transformed by the generator into meaningful high-dimensional images. In particular, the proposed detector exploits the inversion of the GAN synthesis process: given a face image under investigation, we identify the point in the GAN latent space which more closely reconstructs it; we project the vector back into the image space, and we compare the resulting image with the actual one. Through experimental tests on widely known datasets (including FFHQ, CelebA, LFW, and Caltech), we demonstrate that real faces can be accurately discriminated from GAN-generated ones by properly capturing the facial traits through different feature representations. In particular, features based on facial landmarks fed to a Support Vector Machine consistently yield a global accuracy of above 88% for each dataset. Furthermore, we experimentally prove that the proposed detector is robust concerning routinely applied post-processing operations.