DeepShield: AI-Powered Deepfake Detection

The growing pervasiveness of deepfake technology is enormous in its threats to digital integrity, cybersecurity, and the diffusion of misinformation. Public opinion can be manipulated by deepfake media, identity authentication can be challenged, and digital trust can be harmed. To curb such threats, we introduce DeepShield, a new AI-powered deepfake detection system that combines spatial and temporal analysis for strong forgery detection. Our approach integrates a ResNeXt-based Convolutional Neural Network (CNN) for extracting spatial features and a Long Short-Term Memory (LSTM) network for detecting temporal abnormalities between video frames. DeepShield is trained on benchmark datasets such as FaceForensics++ and DFDC and reaches an accuracy of $\mathbf{8 1. 9 7 \%}$ with $\mathbf{8 3. 4 \%}$ recall and $\mathbf{8 0. 4 \%}$ F1-score, all while having good generalization. Though not surpassing state-of-the-art models in bare accuracy, DeepShield prioritizes scalability, robustness against unseen manipulations, and deployability. We also provide future directions such as transformer-based architecture and multimodal detection, thus positioning DeepShield as a step towards robust real-time video authentication in digital forensics and cybersecurity.