The image forgery method based on deep learning can generate images which are difficult to distinguish with the human eye. Once the technology is abused to produce fake images and videos, it will have a serious negative impact on a country's politics, economy, and culture, as well as the social life and personal privacy. To solve the problem, a Deepfake detection method based on autoencoder was proposed. Firstly, the Gaussian filtering was used to preprocess the image, and the high-frequency information was extracted as the input of the model. Secondly, the autoencoder was used to extract features from the image. In order to obtain better classification effect, an attention mechanism module was added to the encoder. Finally, it was proved by the ablation experiments that the proposed preprocessing method and the addition of attention mechanism module were helpful for the Deepfake image detection. Experimental results show that, compared with ResNet50, Xception and InceptionV3, the proposed method can effectively detect images forged by multiple generation methods when the dataset has a small sample size and contains multiple scenes, and its average accuracy is up to 97.10%, which is significantly better than those of the comparison methods, and its generalization performance is also significantly better than those of the comparison methods.