Deep Fake Detection

The rapid advancement of generative adversarial networks (GANs) and other AI-driven synthesis techniques, deepfake videos have emerged as a significant threat to digital media integrity, enabling the creation of highly realistic but fake video content. These manipulated videos can be used maliciously in disinformation campaigns, identity theft, and other cybercrimes, making their detection a critical challenge. This paper presents a deep learning-based approach for deepfake video detection that leverages both spatial artifacts and temporal inconsistencies introduced during the manipulation process. The proposed method utilizes a hybrid architecture combining convolutional neural networks (CNNs) for frame-level feature extraction and recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, to capture temporal dependencies across frames. We evaluate our method on widely used benchmark datasets including FaceForensics++, Celeb-DF, and DFDC, demonstrating superior accuracy, robustness, and generalization capability against multiple types of deepfake generation techniques. The experimental results validate the effectiveness of our approach in real-world scenarios, highlighting its potential for deployment in digital forensics, content moderation, and media verification systems.