Exploring Deepfake Image Forensics: The Role of CNNs, RNNs, GANs and Vision Transformers in Synthetic Media Detection

The advancement of deepfake technology, which is the ability to produce synthetic media of convincingly real likenesses, has created enormous hurdles in the verification and security of digital content. In this light, several deep learning techniques have been employed in identifying deepfake content and lessening its effects. The paper looks at the most recent progress made in deepfake image forensics, especially Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), and Vision Transformers (ViTs). Models based on these CNN, particularly VGG16, VGG19 and ResNet50, are quite useful in the detection of deepfakes by spatial analysis features while time series videos are well dealt with by RNN for pattern anomalies. In addition, deep learning networks Generative Adversarial Networks that are commonly used for elaborating on deepfakes have also been incorporated in the creation of counter diagnostic models to improve the detection. Hybrid approaches using CNNs and ViTs are also becoming popular in deepfake image/video detection. In addition, systems with multiple detection techniques and different preprocessing methods have resulted in better performance of the system. The paper finally argues that there will always be a need for new designs by comparing the efficiency of deepfake detection techniques, The existing systems may grow old due to the advances in new deepfakes systems.