Evolutionary Trigger Set Generation for DNN Black-Box Watermarking

Abstract

The commercialization of deep learning creates a compelling need for intellectual property (IP) protection. Deep neural network (DNN) watermarking has been proposed as a promising tool to help model owners prove ownership and fight piracy. A popular approach of watermarking is to train a DNN to recognize images with certain \textit{trigger} patterns. In this paper, we propose a novel evolutionary algorithm-based method to generate and optimize trigger patterns. Our method brings a siginificant reduction in false positive rates, leading to compelling proof of ownership. At the same time, it maintains the robustness of the watermark against attacks. We compare our method with the prior art and demonstrate its effectiveness on popular models and datasets.

References in corpus (3)

• Embedding Watermarks into Deep Neural Networks
• Adversarial Frontier Stitching for Remote Neural Network Watermarking
• DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models

Cited by in corpus (3)

• Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations
• Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks
• A survey of deep neural network watermarking techniques