Embedding unique identifiers into AI models would BEST help with:

Embedding unique identifiers into AI models, a technique known as digital watermarking or fingerprinting, is a primary method for protecting the intellectual property (IP) of the model. This unique marker serves as a proof of origin, allowing the owner to track the model's distribution and assert ownership if it is stolen, leaked, or used without authorization. It directly links the model artifact back to its creator or legal owner, which is crucial for provenance, accountability, and IP rights enforcement. This technique does not inherently provide security against unauthorized access, eliminate bias, or detect adversarial attacks but focuses specifically on ownership verification.

A. Preventing unauthorized access is managed by access control systems (e.g., authentication, authorization), not by an embedded identifier.

C. Eliminating AI system biases is addressed through data balancing, algorithmic fairness techniques, and careful model design, not by a unique ID.

D. Detecting adversarial attacks requires specific defense mechanisms designed to identify and mitigate malicious inputs, which is unrelated to a static identifier.

1. Zhang

J.

Gu

D.

Jang

J.

Wu

H.

Stoecklin

M. P.

& Chen

G. (2018). Protecting intellectual property of deep neural networks with watermarking. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security (ASIA CCS '18). The abstract states

"we propose a new methodology to watermark a deep neural network for the purpose of intellectual property protection... to verify the ownership and track the pirated copies." (DOI: https://doi.org/10.1145/3196494.3196550)

2. Adi

Y.

Baum

C.

Cisse

M.

Pinkas

B.

& Keshet

J. (2018). Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring. In 27th USENIX Security Symposium (USENIX Security 18). Section 1

Introduction

paragraph 2

explains that watermarking allows a model owner "to prove their ownership over the model" if it is stolen and deployed by an adversary.

3. Carnegie Mellon University

ECE Course 18-739: Security and Fairness of Deep Learning. Lecture 15

"Model Stealing and Watermarking

" describes watermarking as a technique to "Embed a 'signature' in a model to identify the owner" and "prove ownership of a stolen model."