Approaching Deepfake Detection Models

Abstract

The combination of AI and cloud computing has led to the development of advanced techniques for manipulating audio, video, and images. Unfortunately, this has also resulted in a rise in deepfakes - manipulated media that can be incredibly convincing and difficult to detect. Thankfully, there are several countermeasures available to help combat this issue. One such measure is media confirmation, which involves verifying the authenticity of the media through various means such as metadata analysis or source verification. Another approach is media provenance, which focuses on tracing the origin of the media to determine if it has been manipulated. Finally, deepfake discovery utilizes multi-modal detection techniques that combine manual and algorithmic methods to identify manipulated media. The ultimate goal of this chapter is to automate the detection of deepfake videos using these advanced models in real-world scenarios. By doing so, we can help protect against the damaging effects that deepfakes can have on individuals and society as a whole.