Security in the Age of Deepfakes: Detecting Manipulated Media with Neural Networks

The proliferation of deepfake technology presents a substantial peril to both individuals and society, underscoring the urgent requirement for the development of effective deepfake detection techniques. This study introduces a novel methodology that combines convolutional neural networks (CNNs) and deep convolutional generative adversarial networks (DCGANs) in order to improve the detection of deepfake videos. In this study, we present the findings of our research, which involved a comparative analysis of the CNN, recurrent neural network (RNN), and CNN-DCGAN models. The performance evaluation was conducted based on several metrics, including accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and Cohen’s Kappa. The accuracy of the DCGAN model reached 99.2%, establishing a benchmark for comparative analysis. Although the CNN and RNN models demonstrate satisfactory performance, the hybrid approach of CNN-DCGAN surpasses them in various metrics, thereby showcasing its potential for effective identification of deepfakes. This study emphasizes the efficacy of integrating convolutional neural networks (CNNs) for extracting features and deep convolutional generative adversarial networks (DCGANs) for generating synthetic data. This advancement holds promise for the development of more sophisticated and dependable deepfake detection systems, particularly in a time where the risk of manipulated media is escalating.