The Game Between the Defender and the Attacker

A novel game-theoretic framework for analyzing black-box DNN watermarking is a technique aimed at protecting the intellectual property of deep neural networks. 

The authors believe that existing trial-and-error methods have their limitations, so they propose a game in which a model defender and an attacker compete against one another. 

During the time that the attacker is attempting to remove or disrupt the watermark, the defender is using trigger samples to embed a watermark into the DNN model. 

The payoff functions for both players are defined by them, taking into consideration a variety of factors including the performance of the model, the accuracy of the watermark detection, and the costs associated with embedding and removing the watermark. 

Through careful examination of the game, they are able to ascertain the most effective strategies for both the defender and the attacker. 

The defender’s optimal strategy depends on the difference in robustness between different watermarked models and the difference in attack strength, which provide valuable insights into the design of more robust and secure DNN watermarking schemes. 

Evaluation of the safety of DNN watermarking systems can be carried out using a rigorous framework that is provided by the game-theoretic approach. 

Researchers are able to develop more effective defense mechanisms and anticipate potential attacks if they have a better understanding of the strategic interactions that take place between the offender and the defender. 

The results of this study make a significant contribution to the development of DNN watermarking and improve the safeguarding of intellectual property in the field of deep learning.

The purpose of this paper is to investigate trigger-based model watermarking by utilizing a relatively new framework for partial cooperation games. 

Recognizing the coexistence of cooperative and competitive aspects, the authors analyze the interplay between defender and attacker, considering economic factors in their payoff function. 

The study reveals that the optimal defense strategy hinges on the robustness variations among watermarked models and the efficacy disparities among attacks, which emphasizes the critical need for robust watermarked models against real-world attacks. 

Future research will delve into the impact of trigger set selection on model performance in real-world settings, investigate practical implementations to validate and extend the framework, and explore generative model watermarking games to further enrich watermarking theory.

Also Read:

Kaaviya
Kaaviyahttps://cyberpress.org/
Kaaviya is a Security Editor and fellow reporter with Cyber Press. She is covering various cyber security incidents happening in the Cyber Space.

Recent Articles

Related Stories

LEAVE A REPLY

Please enter your comment!
Please enter your name here