Protecting Deep Learning Model Copyrights With Adversarial Example-Free Reuse Detection.

Area of Science:

• Artificial Intelligence
• Computer Science
• Machine Learning

Background:

• Deep neural networks (DNNs) offer efficiency through model reuse, but this raises copyright concerns.
• Existing DNN copyright protection methods face limitations, especially with altered model architectures (heterogeneous reuse) and in black-box scenarios.
• Current fingerprinting techniques often rely on difficult-to-generate adversarial examples.

Purpose of the Study:

• To develop an effective and versatile DNN copyright protection technique.
• To address the limitations of existing methods in handling heterogeneous reuse and black-box settings.
• To introduce a novel approach for detecting DNN reuse relations using neuron functionality.

Main Methods:

• Proposed a neuron functionality analysis-based reuse detector (NFARD).
• Developed NF-based distance metrics for both white-box and black-box detection.
• Implemented a linear transformation method to handle heterogeneous DNN reuse cases.
• Created the Reuse Zoo benchmark for evaluating reuse detection methods.

Main Results:

• NFARD achieved high F1 scores (0.984 in black-box, 1.0 in white-box) on the Reuse Zoo benchmark.
• The method successfully detects reuse even with altered model architectures.
• NFARD generates test suites significantly faster (2-99x) than previous approaches.
• This is the first adversarial example-free method leveraging neuron functionality for DNN copyright protection.

Conclusions:

• NFARD provides a robust, efficient, and versatile solution for DNN copyright protection.
• The neuron functionality analysis approach overcomes key limitations of prior methods.
• The Reuse Zoo benchmark facilitates future research in DNN reuse detection.