Deepfake Detection using Biological Features: A Survey
It addresses the problem of detecting manipulated media for security and legal applications, but is incremental as it surveys existing methods without introducing new techniques.
This survey reviews deepfake detection methods using biological features like eye blinking and heartbeat detection, highlighting the increasing difficulty of distinguishing deepfakes from natural images as technology advances.
Deepfake is a deep learning-based technique that makes it easy to change or modify images and videos. In investigations and court, visual evidence is commonly employed, but these pieces of evidence may now be suspect due to technological advancements in deepfake. Deepfakes have been used to blackmail individuals, plan terrorist attacks, disseminate false information, defame individuals, and foment political turmoil. This study describes the history of deepfake, its development and detection, and the challenges based on physiological measurements such as eyebrow recognition, eye blinking detection, eye movement detection, ear and mouth detection, and heartbeat detection. The study also proposes a scope in this field and compares the different biological features and their classifiers. Deepfakes are created using the generative adversarial network (GANs) model, and were once easy to detect by humans due to visible artifacts. However, as technology has advanced, deepfakes have become highly indistinguishable from natural images, making it important to review detection methods.