Conjectured that monitoring neuron behavior could also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the facial manipulation detection system. Their proposed approach, named FakeSpoter, extracted as features neuron coverage behaviors of real and fake faces from deep face recognition systems, and then trained a SVM for the final classification. The authors tested their proposed approach using real faces from CelebA-HQ and FFHQ databases and synthetic faces created through InterFaceGAN and StyleGAN, achieving for the best performance a final 84.7% fake detection accuracy using the FaceNet model.