Detecting Deepfake Faces with CNN and LSTM

In recent years, the advent of deepfake technology has posed significant challenges to the veracity of digital content. Originally emerging as an offshoot of advancements in generative modelling, deep fakes have evolved into sophisticated tools capable of creating hyper-realistic yet entirely synthetic facial content in videos and images. These manipulations pose serious threats across various sectors including journalism, law enforcement, politics, and social media by enabling the spread of misinformation, identity fraud, and reputational damage. To address these growing concerns, this investigation proposes an integrated deepfake detection system utilizing Convolutional Neural Networks. Convolutional Neural Networks (CNNs) are employed for the extraction of spatial features across individual frames, enabling the identification of discrepancies such as artificial textures or visual anomalies. LSTMs complement this by modeling temporal dependencies across frame sequences to detect anomalies in facial movements and expressions. The combined framework enables the system to assess both static and dynamic patterns typical of deepfake manipulations. The model has undergone training and testing on a comprehensive dataset containing authentic and manipulated media. demonstrating high detection accuracy. Experimental evaluation reveals that the CNN-LSTM hybrid outperforms traditional static analysis models in identifying complex temporal inconsistencies, making it highly effective for video-based deepfake detection. Visualization modules and a user-friendly interface further support real-time use cases, enhancing interpretability and deployment potential in real-world scenarios.