AI/ML-Based Solution for Detecting Face-Swap Deepfakes

The rapid advancement of deepfake technology, particularly in face-swap applications, poses significant challenges to the integrity of digital media and personal security. This research introduces a hybrid system using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to detect deepfakes. The model combines spatial and temporal feature analysis to spot inconsistencies in manipulated media. Facial landmarks are detected using the Dlib library, while classifiers like Support Vector Machines (SVM) and Artificial Neural Networks (ANN) enhance detection accuracy. Data augmentation and bidirectional LSTM processing improve the system’s robustness in real-world scenarios. By leveraging AI-driven techniques, the model offers a reliable solution to counter the growing misuse of deepfake technology, ensuring high precision and adaptability.