Deepfake Detection System Using a Hybrid Model
S Sudharson, Priyanka Kokil, D. Sasikala, Penta Aravinda Swamy · 2023
There has been a rapid advancement in deep fake technology in recent years, which makes it increasingly difficult to detect fake images, especially those involving human faces, which have become difficult to detect. The traditional methods of detecting deep fake images are usually based on a human-crafted feature or a rule-based system, which can be easily beaten with simple cheating techniques. Intending to detect deepfake face images, we present a novel method that uses CapsNet and EfficientNet as feature extractors to detect deepfake face images. They are well suited to detect deepfake images that have undergone geometric transformations or variations as they can learn the spatial relationship between those features. Using EfficientNet, a powerful and efficient deep learning architecture, as a feature extractor, we extract high-level characteristics from an input image to create a more accurate image analysis.Based on our experiments,it was found that this method demonstrated the capability to detect various types of deep fake images with an accuracy rate of 99.64%, including those fabricated through face swapping and re-enacting. This method could be applied to a wide range of real-world applications, including in media forensics, law enforcement, and online media verification, so that it could be applied in many different contexts.