Humans versus machines: a deepfake detection faceoff
Machine learning (ML) models for deepfake detection are important for countering the threat of such videos. However, human detection is also critical because automated approaches may not always be available to people online. This study compares ML models versus humans for deepfake detection. Results...
Saved in:
Main Authors: | Goh Dion Hoe-Lian, Pan, Jonathan, Lee, Chei Sian |
---|---|
Other Authors: | Wee Kim Wee School of Communication and Information |
Format: | Article |
Language: | English |
Published: |
2025
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/181973 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Mobile DeepFake detection using EfficientNet and facial landmarks
by: Toh, Dion Siyong
Published: (2024) -
Countering malicious deepfakes: survey, battleground, and horizon
by: Xu, Felix Juefei, et al.
Published: (2022) -
Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence
by: Abbas, Fakhar, et al.
Published: (2024) -
A continual deepfake detection benchmark: Dataset, methods, and essentials
by: LI, Chuqiao, et al.
Published: (2021) -
Enhancing MobileNet for efficient deepfake detection: a hybrid approach with GhostNet and ShuffleNet
by: Lee, Zheng Xuan
Published: (2024)