Review of Deep Fake Detection Using Deep Learning Convolutional, Recurrent, and Graph Networks

The exponential growth in the generation and dissemination of deep fake content has resulted in huge challenges to the integrity and trustworthiness of digital media. The current paper tries to meet an unmet need for robust and reliable deep fake detection techniques by reviewing the current literature and meta-analyzing the existing methods. This study thoroughly assesses various deep fake detection models; their methodologies; strengths; and weaknesses, and performance metrics, such as accuracy, precision, recall, and F1-scores. The paper provides a review and critical analysis of methods using a diverse set of techniques, ranging from CNNs, RNNs, GANs, and hybrid approaches. CNNs, especially those using transfer learning from pre-trained models like VGG16, ResNet, and InceptionV3, have demonstrated image-based deep fake detection with high accuracy because of their capability in effectively capturing spatial features. RNNs, especially the Long Short-Term Memory (LSTM) architecture, are applied to perform video-based detection, leveraging the distinctive features of analyzing data over a temporal sequence and identifying inconsistencies over time. In the beginning, GAN-based methods used to generate deep fakes have now been repurposed for detection. This work provides a technique to repurpose GAN-based methods for detection using the capability of such models to distinguish between real and synthetic data using adversarial training. Hybrid architectures that integrate several such neural network models hold promise for improving detection performance through combined strengths. This comparative analysis demonstrates that CNN-based methods are typically the best-performing methods for static image analysis, while RNN is more effective for the video sequence. GAN-based methods are capable of adapting to new deep fake generation techniques, but do so with increased computational complexity. Hybrid methods have increased complexity but present a better trade-off between accuracy and computational efficiency. The impact of such a detailed review is two-fold. By systematically categorizing and reviewing the existing methods for deep fake detection, the current study brings out a clear view of the state-of-the-art, guiding researchers and practitioners in the selection of relevant techniques with a view to specific application requirements. Further, the identification of limitations and gaps in the currently existing methods lays out research directions for the future, leading toward the development of more robust and scalable detection systems. Ultimately, this work contributes to enhancing the security and reliability of digital media, fostering greater public trust in digital content authenticity.