Deepfake detection with dual-mode swin transformer: Multi-scale feature learning and local ambiguity mitigation

This research paper presents a method for detecting deepfakes (synthetic videos or images created by AI to look realistic) using a dual-mode Swin Transformer, which is a type of neural network architecture. The approach uses multi-scale feature learning (analyzing visual details at different zoom levels) and local ambiguity mitigation (reducing confusion in uncertain areas) to improve detection accuracy. This is a technical contribution to security research, not a response to an existing vulnerability or security incident.