Explore a 19-minute IEEE conference talk on a testing framework for copyright protection of deep learning models. Delve into the DEEPJUDGE framework, multi-level testing metrics, and test case generation in both black-box and white-box settings. Examine experiments against model fine-tuning, pruning, extraction, and adaptive attackers. Gain insights into DNN watermarking and the challenges of protecting intellectual property in the field of artificial intelligence.
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Overview
Syllabus
Intro
Deep Learning Models
DL Model Copyright Protection
Model Thief
DNN Watermarking
DEEPJUDGE Framework
Multi-level Testing Metrics
Metrics: Property-level
Metrics: Neuron-level
Metrics: Layer-level
Test Case Generation
Generation: Black-box Setting
Generation: White-box Setting
Final Judgement
Experiments
Against Model Fine-tuning & Pruning
Combined Visualization
Comparison
Against Model Extraction
Adaptive Attackers
Summary & Discussion
Taught by
IEEE Symposium on Security and Privacy
