The new development in Artificial Intelligence, has significantly improved the quality and efficiency in generating fake face images; for example, the face manipulations by DeepFake is so realistic that it is difficult to distinguish the authenticity—either automatically or by humans. In order to enhance the efficiency of distinguishing facial images generated by AI from real facial ones, a novel model has been developed based on deep learning and ELA detection, which is related to entropy and information theory, such as cross-entropy loss function in the final Softmax layer, normalized mutual information in image preprocessing and some applications of encoder based on information theory. Due to the limitations of computing resources and production time, DeepFake algorithm can only generate limited resolutions, resulting in two different image compression ratios between the fake face area as the foreground and the original area as the background, which would leave distinctive artifacts. By using the error level analysis detection method, we can detect the presence or absence of different image compression ratios, and then use CNN to detect whether the image is fake. Experiments show that the training efficiency of CNN model can be significantly improved by using the ELA method. And the detection accuracy rate can reach more than 97% based on CNN architecture of this method. Compared to the state-of-the-art models, the proposed model has the advantages such as fewer layers, shorter training time and higher efficiency.