Deepfake Detection Using Hybrid Deep Learning Approach

In the age of artificial intelligence, deepfakes—man-made media created with the help of deep learning algorithms—present a severe threat to the authenticity and credibility of digital information. Although these technologies present creative value in entertainment and teaching, their misapplication can result in spreading misinformation, identity theft, and privacy invasion. This paper introduces a hybrid deep learning method for deepfake detection that integrates Convolutional Neural Networks (CNNs) for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal sequence modeling. The introduced framework takes advantage of transfer learning from pre-trained CNN models and attention mechanisms to improve detection accuracy and generalizability. Experimental verification is scheduled using benchmark datasets like FaceForensics++ and DeepFake Detection Challenge (DFDC) to be robust enough against various manipulation methods. The goal is to create a scalable, precise, and ethically sound detection system to counteract changing deepfake attacks.