Deepfake Video Detection using Neural Networks

In recent months, advancements in free deep learning-based software tools have made it easier to create convincing face swaps in videos, often leaving minimal traces of manipulation. This phenomenon is commonly known as “DeepFake� (DF) videos. While manipulations of digital videos using visual effects have been demonstrated for decades, recent progress in deep learning has significantly enhanced the realism of fake content and the ease with which it can be generated. These artificially intelligent tools, commonly referred to as AI-synthesized media or DF, have simplified the creation process. However, detecting DeepFake videos poses a significant challenge. Despite the simplicity of generating DF using AI tools, training algorithms to identify them is not straightforward. To address this issue, we have taken a step forward by employing Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) for detection. Our system utilizes a CNN to extract features at the frame level. These features are then employed to train an RNN, which learns to classify whether a video has undergone manipulation. The system is capable of detecting temporal inconsistencies between frames introduced by the tools used in DF creation. To evaluate the effectiveness of our approach, we tested it against a large set of fake videos collected from a standard dataset. The results demonstrate the competitiveness of our system, achieved through a simple architecture.