Deep Fake Video Detection using Convolutional Neural Networks

In recent months, the rise of free, deep learning- based software tools has significantly simplified the creation of highly realistic face-swapped videos, commonly known as “Deep Fakes” (DFs). While video manipulation through visual effects has been practiced for decades, recent advancements in deep learning have drastically enhanced the realism and accessibility of such synthetic content. Creating DFs using AI tools has become relatively straightforward; however, detecting these manipulations remains a major challenge. This is due to the complexity involved in training algorithms to recognize subtle and often imperceptible signs of tampering. In this work, we present a deep learning-based approach that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to detect Deep Fake videos. Our system employs CNNs to extract spatial features at the frame level, which are then passed to an RNN that captures temporal inconsistencies between frames introduced during Deep Fake generation. We evaluate our method on a large dataset of manipulated videos and demonstrate that our approach achieves competitive results using a relatively simple architecture.