FakeFaceDiscriminator: Discrimination of AI-Synthesized Fake Faces

In recent years, the rapid improvement of deep learning technologies, particularly Generative Adversarial Net-works, has led to the proliferation of high-quality synthetic facial images and videos, commonly known as deepfakes. This study aims to evaluate and compare the performance of three prominent deep learning models - ResN et, EfficientNet, and Xception - in detecting synthetic faces. Using the Deepfake Detection Challenge and FaceForensics++ datasets, we system-atically assess each model's capability to handle diverse and challenging scenarios, including blurred and dark images. Data augmentation techniques, such as random blurring, brightness adjustment, and contrast enhancement, were employed to im-prove the models' robustness. Additionally, we applied model- specific optimizations, including the integration of Squeeze-and- Excitation blocks in Res Net, compound scaling in EfficientNet, and multi-scale feature fusion in Xception. These enhancements significantly improved the models' accuracy and resilience against low-quality synthetic data. Our results indicate that EfficientNet and Xception outperform ResNet in both general and adverse conditions, with EfficientNet excelling in high-resolution image processing and Xception showing superior performance in fine- grained feature extraction. Furthermore, the introduction of pre- trained weights, multitask learning frameworks, and dynamic learning rate adjustments during training contributed to the models' enhanced performance.