Conference
Cosine Model Watermarking against Ensemble Distillation
Abstract
Many model watermarking methods have been developed to prevent valuable deployed commercial models from being stealthily stolen by model distillations. However, watermarks produced by most existing model watermarking methods can be easily evaded by ensemble distillation, because averaging the outputs of multiple ensembled models can significantly reduce or even erase the watermarks. In this paper, we focus on tackling the challenging task of …
Authors
Charette L; Chu L; Chen Y; Pei J; Wang L; Zhang Y
Volume
36
Pagination
pp. 9512-9520
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
DOI
10.1609/aaai.v36i9.21184
Conference proceedings
Proceedings of the AAAI Conference on Artificial Intelligence
Issue
9
ISSN
2159-5399