Technical Research on Deep Learning Generative Adversarial Networks for Fake News Sample Augmentation and Detection

We propose a novel deep learning method to detect fake news, addressing the threat to social stability and information security. Our approach uses conditional GANs for data augmentation and fuses multi-modal features with hierarchical attention mechanisms. Experimental results show our method outperforms existing techniques in accuracy and F1-score. By compressing and quantizing the model, we reduce resource consumption for practical applications. This research offers new technical support for combating fake news and maintaining a healthy online environment.