© 2020 IEEE.Deepfake technology allows humans to manipulate images and videos using deep learning technology. The results from deepfakes are very difficult to distinguish using ordinary vision. Many algorithms are built to detect deepfake content in images and videos. There are several approaches in deepfake detection, including a visual feature-based approach, a local feature-based approach, a deep feature-based approach and a temporal feature-based approach. The main challenge in developing deepfake detection algorithms is the variety of existing deepfake models in both images and videos. Another challenge is that deepfake technology is still evolving, making deepfake images and videos look more realistic and harder to detect.

Comparative studies,Detection algorithm,Feature based approaches,Learning technology,Local feature,Temporal features,Video detection,Visual feature

autoencoder,deep learning,deepfake,Generative Adversarial Networks

https://doi.org/10.1109/ICOIACT50329.2020.9331963