Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM
This addresses the need for detecting deepfakes to prevent the spread of false information on social media, but appears incremental as it combines existing methods without novel breakthroughs.
The paper tackles the problem of deepfake detection to combat misinformation by proposing a new detection schema using CNN and SVM, but no concrete results or numbers are provided.
Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one face with a computer generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms.