Arbitrary-Scale Super-Resolution for Remote Sensing Images with Multi-Branch Feature Enhancement and Scale-Specific Dictionary Attention

With the widespread application of deep learning technologies such as Convolutional Neural Networks CNNs and Generative Adversarial Networks GANs , facial forgery techniques have matured rapidly, bringing innovative applications to multiple fields while also raising serious security concerns. To address this challenge, researchers have developed various deepfake detectors. However, these detectors have shown significant vulnerabilities when faced with adversarial attacks. This study aims to systematically evaluate the performance of deepfake detectors under adversarial attacks and test the effectiveness of various defense methods. Through large-scale experiments, we analyzed the performance of different types of detectors under various adversarial attacks and assessed the efficacy of existing defense strategies. The results indicate that while some defense methods perform well in specific scenarios, the overall robustness of detectors still needs improvement. This research not only deepens our understanding of adversarial robustness in deepfake detection but also provides important experimental evidence and theoretical guidance for developing more effective defense strategies.