Position-Aware CNN-Transformer Model for Deepfake Detection

This study tackles the challenges of Deepfake detection posed by modern generative models and introduces an integrated architecture that combines Convolutional Neural Network (CNN) with Transformer, enhanced by position-aware modules to improve detection accuracy. While Transformer inherently incorporates positional encoding, CNN lacks explicit spatial modeling capabilities. To address this, our position-aware mechanism fuses RGB images and CNN-derived feature maps with positional encoding information using position-aware attention modules. This integration strengthens the backbone network’s ability to capture and interpret spatial cues. To train and evaluate our model, we constructed a comprehensive dataset of 80,000 facial images—comprising 40,000 AI-generated images from four cutting-edge generative models (StyleGAN3, EG3D, Stable Diffusion XL, and DALL-E 3 XL v2) and 40,000 real images sourced from CelebA-HQ and FFHQ. Experimental results show that our model consistently outperforms baseline approaches on images generated by StyleGAN3, EG3D, Stable Diffusion XL, and DALL-E 3 XL, achieving accuracy gains of 8.49%, 2.93%, 2.40%, and 4.40%, respectively. These findings highlight the model’s superior performance, strong generalization capability, and robustness in detecting Deepfakes generated by state-of-the-art AI models.