FFDL: Feature Fusion-Based Deep Learning Method Utilizing Federated Learning for Forged Face Detection
The widespread adoption of advanced technologies may be responsible for the extensive dissemination of forged photographs and videos on the Internet. This could potentially result in the proliferation of fraudulent identities online, raising safety concerns in society. The traditional method for det...
Saved in:
Main Authors: | Vinay Gautam, Gaganpreet Kaur, Meena Malik, Ankush Pawar, Akansha Singh, Krishna Kant Singh, S. S. Askar, Mohamed Abouhawwash |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10816324/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on privacy-preserving learning machines
by: Yi-zhang JIANG, et al.
Published: (2016-06-01) -
A reliable and privacy-preserved federated learning framework for real-time smoking prediction in healthcare
by: Siddhesh Fuladi, et al.
Published: (2025-01-01) -
Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions
by: R. Vijay Anand, et al.
Published: (2025-01-01) -
Efficient secure federated learning aggregation framework based on homomorphic encryption
by: Shengxing YU, et al.
Published: (2023-01-01) -
Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption
by: Hong Cheng
Published: (2025-01-01)