There has been an increasing interest in developing methods for image representation learning, focused in particular on training deep neural networks to synthesize images. Generative adversarial networks (GANs) are used to apply face aging, to generate new viewpoints, or to alter face attributes like skin color. For forensics specifically on faces, some methods have been proposed to distinguish computer generated faces from natural ones and to detect face retouching. We propose to investigate techniques based on perceptual judgments to detect image/video manipulation produced by deep learning architectures. The main objectives of this study are: (1) To develop technique to make a distinction between Computer Generated and photographic faces based on Facial Expressions Analysis; (2) To develop entropy-based technique for forgery detection in Computer Generated (CG) human faces. The results show differences between emotions in both original and altered videos. These computed results were large and statistically significant. The results show that the entropy value for the altered videos is reduced comparing with the value of the original videos. Histograms of original frames have heavy tailed distribution, while in case of altered frames; the histograms are sharper due to the tiny values of images vertical and horizontal edges.