Face Forgery Detection Combined with Deep Forgery Features Comparison
With the continuous development of artificial intelligence-generated content technology,the diversity of forgery techniques presents significant challenges to existing detection methods.Most current detection methods are based on facial forgery features extracted by different advanced convolutional neural networks.However,these methods are trained on datasets containing known forgery techniques,and their generalization capabilities are inadequate to handle images forged by unknown methods.Therefore,a face forgery detection method combined with deep forgery features comparison is proposed,and it exhibits excellent adaptability to unknown forgery techniques.The proposed approach consists of two stages.First,similar features of different forgery techniques are explored,and a meta-learning-based similar feature fusion network is introduced.This network leverages the learning capabilities of meta-learning to capture the similar features among different forgery methods.Second,unique forgery features specific to individual task are taken into account,and a task-specific uniqueness fine-tuning method is proposed to enhance the adaptability of the model to unknown forgery techniques.Cross-manipulation testing demonstrates that the proposed method improves the performance with superior detection capability against attacks from unknown forgery techniques.
Face Forgery DetectionDeep FakeMeta-LearningSimilar Feature FusionForgery Feature Mining
万方数据
维普
