Despite tremendous success in many application scenarios, the training and
inference costs of using deep learning are also rapidly increasing over time.
The lottery ticket hypothesis (LTH) emerges as a promising framework to
leverage a special sparse subnetwork (i.e., winning ticket) instead of a full
model for both training and inference, that can lower both costs without
sacrificing the performance. The main resource bottleneck of LTH is however the
extraordinary cost to find the sparse mask of the winning ticket. That makes
the found winning ticket become a valuable asset to the owners, highlighting
the necessity of protecting its copyright. Our setting adds a new dimension to
the recently soaring interest in protecting against the intellectual property
(IP) infringement of deep models and verifying their ownerships, since they
take owners' massive/unique resources to develop or train. While existing
methods explored encrypted weights or predictions, we investigate a unique way
to leverage sparse topological information to perform lottery verification, by
developing several graph-based signatures that can be embedded as credentials.
By further combining trigger set-based methods, our proposal can work in both
white-box and black-box verification scenarios. Through extensive experiments,
we demonstrate the effectiveness of lottery verification in diverse models
(ResNet-20, ResNet-18, ResNet-50) on CIFAR-10 and CIFAR-100. Specifically, our
verification is shown to be robust to removal attacks such as model fine-tuning
and pruning, as well as several ambiguity attacks. Our codes are available at
this https URL

本文研究使用基于图形签名和触发器集的稀疏拓扑信息进行彩票验证，以防止知识产权侵权和确认所有权。实验表明，该方法对不同模型和攻击有鲁棒性。