Deep Fake Detection for Preventing Audio and Video Frauds Using Advanced Deep Learning Techniques

Deepfakes allow for the automated gen- eration of fake video content, often accomplished through the use of generative adversarial networks. To address the increasing issue of deepfakes, this study focuses on constructing a model that incorporates advanced techniques. The researchers combined the ResNeXt, Long Short-Term Memory (LSTM), and ResNet architectures, selecting them based on their effectiveness in handling complex visual data and capturing temporal dependencies. Prior to detection, the dataset underwent pre-processing using the Multi-Task Cascaded Convolutional Neural Network (MTCNN), which facilitated the accurate extraction of facial regions. Importantly, the study evaluated the model across three diverse and significant datasets: the Face Forensics++ dataset (FF-DF), the Celeb-DF dataset, and the Facebook Deepfake Detection Challenge (DFDC) dataset. This comprehensive evaluation en- sured the model's ability to generalize and its suitability for real-world scenarios, as demonstrated by its exceptional detection accuracy. The combination of models employed in this study yielded highly accurate results and remained robust in the face of evolving deepfake technology.