Jun 3, 2019 · In this paper, we introduce DAWN (Dynamic Adversarial Watermarking of Neural Networks), the first approach to use watermarking to deter model extraction IP ...
Oct 17, 2021 · In this paper, we introduce DAWN (Dynamic Adversarial Watermarking of Neural Networks), the first approach to use watermarking to deter model extraction theft.
This repo contains code that allows you to reproduce experiments for the watermarking scheme presented in DAWN: Dynamic Adversarial Watermarking of Neural ...
In this paper, we introduce DAWN (Dynamic Adversarial Watermarking of Neural Networks), the first approach to use watermarking to deter IP theft via model ...
Jun 3, 2019 · We show that. DAWN is resilient against two state-of-the-art model extraction attacks, effectively watermarking all extracted surrogate models,.
We show that DAWN is resilient against two state-of-the-art model extraction attacks, effectively watermarking all extracted surrogate models, allowing model ...
Oct 24, 2021 · DAWN. (Dynamic Adversarial Watermarking of Neural Networks), is an additional component added in front of a model prediction API. (Sect. 3.2).
Our approach - deter using watermarking: • Return incorrect predictions for selected inputs. • Force adversary to embed watermark while training surrogate.
Missing: Neural Networks.
Watermarking against model extraction attacks in MLaaS. ACM MM 2021. - dawn-dynamic-adversarial-watermarking-of-neural-networks/main.py at master ...
Jun 3, 2019 · DAWN (Dynamic Adversarial Watermarking of Neural Networks), the first approach to use watermarking to deter model extraction theft, ...