Research on deep face forgery detection method based on improved ResNet34
The illegal application of deep face forgery technology has caused serious harm to the people's property secu-rity,and the current forgery video detection accuracy is low and the generalization ability is poor in the face of new forg-ery technology.Aiming at solving the above problems,an attention mechanism is proposed to improve the deep face forg-ery detection method of ResNet34.First,an efficient channel attention module is introduced,which eliminates the need for dimensionality reduction and dimensionality upgrading,thus preserving the information integrity of the original chan-nel features;second,the artifact region in the forged image is extracted and input into the model as a backbone feature,which enhances the model's performance of detecting local features of the face;third,the labeling with the position infor-mation of the artifact region is generated by using the multiscale sliding window and different mixing functions,which fa-cilitates the detection of artifact features by the artifact detection module.The experimental results show that the method of this study has a detection accuracy of 97.88%and an AUC value of 99.84%on the FF++(c23)dataset,and compared with the latest methods,the method of this study has the best generalization ability,which proves the validity of the meth-od proposed in this study.
deep face forgeryResNet34efficient channel attentionartifact detectionmulti-scale sliding window
万方数据
维普
