Abstract: At present,the face detection methods mainly focus on the detection of the authenticity of faces,and there are few studies on the recognition of forgery algorithms,accompanied by poor image disturbance robustness and large resource occupation.What's more,the public face detection datasets have problems such as slow update and few types.In order to solve the above problems,this paper proposes a face forgery algorithm recognition model—Indentiformer,which takes the visual self-attention model as the backbone.It decomposes the position coding fusion block,and then uses Fast Fourier Transformation improved by the Khatri-Rao product to extract the global features.At the same time,the parallel convolutional structure is used to supplement the local feature information and the multi-head attention mechanism is used for fusion to enhance the modeling ability of the model.Finally,the overfitting is reduced by the improved multilayer perceptron based on regularization to realize the recognition of face forgery algorithms.In addition,this paper also constructs a multi-classification dataset of fake faces,including 18 forgery methods such as diffusion model,large model and fusion technology,with a total of more than 410 000 face images,which has better data diversity in true and false mixing.Experimental results show that the Indentifomer model achieves 99.57% and 99.73% AUC in the algorithm recognition multi-classification and true-false discrimination binary classification tasks without increasing resource overhead.In robustness experiments,the AUC decreases by only 4.62% on average,which has high recognition ability and anti-interference ability.

Key words: Face forgery algorithm recognition, Deepfake, Visual self-attention, Face dataset, Multi-classification