Review of Deep Learning Techniques for Deepfake Image Detection

Abstract

Deepfake is an advanced synthetic media technology that generates convincingly authentic yet fake images and videos by modifying a person's likeness. The term "Deepfake" is a blend of "Deep learning" and "Fake," highlighting the use of artificial intelligence and deep learning algorithms in its creation. Deepfake generation involves training models to learn the nuances of facial attributes, expressions, motion, and speech patterns to produce fabricated media indistinguishable from real footage. Deepfakes are often used to manipulate human content, especially the invariant facial regions. The spatial relationship between facial attributes is crucial for creating a convincing, hyperrealistic deepfake output. Subtle inconsistencies in facial features, such as eye spacing, skin color, and mouth shape, can serve as indicators for detecting deepfakes. While many techniques have been developed to detect deepfakes, not all are perfectly accurate for every case. As new deepfake creation methods emerge, existing detection strategies must be continually updated to address these advancements. This paper reviews various deepfake image detection methods and deep learning techniques.