Enhancing Security of Proof-of-Learning Against Spoofing Attacks Using Feature-Based Model Watermarking

Enhancing Security of Proof-of-Learning Against Spoofing Attacks Using Feature-Based Model Watermarking

The rapid advancement of machine learning (ML) technologies necessitates robust security frameworks to protect the integrity of ML model training processes. Proof-of-Learning (PoL) is a critical method for verifying the computational effort in training ML models, while model watermarking is a strate...

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Bibliographic Details
Main Authors: Ozgur Ural, Kenji Yoshigoe
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Proof-of-learning
model watermarking
machine learning security
spoofing attack countermeasures
dual-layered verification
model authenticity
Online Access:https://ieeexplore.ieee.org/document/10741282/
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