Deepfake Detection using Biological Features: A Survey

TL;DR

This survey reviews deepfake technology, focusing on biological feature-based detection methods, their challenges, and the evolution of deepfake generation and detection techniques.

Contribution

It provides a comprehensive overview of physiological measurement-based deepfake detection methods and compares different biological features and classifiers used.

Findings

Deepfakes are increasingly indistinguishable from real images.

Biological features like eye movement and heartbeat are promising detection cues.

Detection methods face challenges due to deepfake quality improvements.

Abstract

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.