In the current detection of the malware family, the local features or global features extracted through the grayscale image of the malware cannot fully describe the malware. Aiming at the problem and to improve the detection effect, a malware detection method based on perceptual hash algorithm and feature fusion was proposed. Firstly, the grayscale image samples of malware were detected through the perceptual hash algorithm, and samples of specific malware families and uncertain malware families were quickly divided. Experimental tests showed that about 67% malwares were able to be detected by the perceptual hash algorithm. Then, the local features of Local Binary Pattern (LBP) and global features of Gist were further extracted for the samples of uncertain families, and the features of merging the above two features were used to classify and detect the malware samples by the machine learning algorithm. Finally, experimental results of the detection of 25 types of malware families show that the detection accuracy is higher when using the fusion feature of LBP and Gist compared to that when using a single feature only, and the proposed method is more efficient in classification and detection than the detection algorithm using machine learning only with the detection speed increased by 93.5%.

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.