Summary of Say No to Freeloader: Protecting Intellectual Property Of Your Deep Model, by Lianyu Wang et al.
Say No to Freeloader: Protecting Intellectual Property of Your Deep Model
by Lianyu Wang, Meng Wang, Huazhu Fu, Daoqiang Zhang
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Model intellectual property (IP) protection has gained significant attention as scientific advancements rely heavily on human intellectual labor and computational expenses. To ensure IP safety for trainers and owners, particularly in domains where ownership verification and applicability authorization are crucial, a novel approach is introduced that proactively prevents the use of well-trained models from unauthorized domains. This paper proposes the Compact Un-transferable Pyramid Isolation Domain (CUPI-Domain) as a barrier against illegal transfers. Inspired by human transitive inference and learning abilities, CUPI-Domain emphasizes distinctive style features of authorized domains to obstruct cross-domain transfers. Novel CUPI-Domain generators select features from both authorized and CUPI-Domain anchors, fusing style and semantic features to generate labeled and style-rich CUPI-Domain. Additionally, external Domain-Information Memory Banks (DIMB) store and update labeled pyramid features for stable domain class features and domain class-wise style features. Style and discriminative loss functions are designed to enhance distinction in style and discriminative features between authorized and unauthorized domains. This paper provides two solutions for utilizing CUPI-Domain based on whether the unauthorized domain is known, including target-specified CUPI-Domain and target-free CUPI-Domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model intellectual property protection is important because it keeps people’s ideas safe. Imagine someone taking a picture you took without your permission! This paper talks about how to stop that from happening with special models called Compact Un-transferable Pyramid Isolation Domains (CUPI-Domains). These domains are like barriers that keep unauthorized people from using authorized models. The authors used human-like learning abilities to create these domains and made them so good that they can’t be copied or moved to other places without permission. This is important because it keeps our ideas and creations safe. |
Keywords
» Artificial intelligence » Attention » Inference
