网络与信息安全学报2025，Vol.11Issue(1)：92-105,14.DOI:10.11959/j.issn.2096-109x.2025009
基于分层结构的多任务对抗样本归因方法
Multi-task adversarial attribution method based on hierarchical structure
摘要
Abstract
Deep neural networks have demonstrated superior performance in various computer vision tasks.How-ever,they have been found to be highly susceptible to adversarial attacks,which involve the addition of perturba-tions to examples during the inference phase that are imperceptible to the human eye.To defend against adversarial attacks,some works have explored the reverse engineering of adversarial examples,known as the adversarial attri-bution problem.By attributing the attack algorithm and victim model used to generate adversarial examples,de-fenders can gain insights into the attacker's knowledge and targets,thereby enabling the design of more effective defense algorithms against corresponding attacks.Existing methods have mostly approached the adversarial attribu-tion problem as a single-task learning problem.However,as the scope of attack algorithms and victim models has expanded,single-task learning has faced the challenge of combinatorial explosion.To improve the accuracy of ad-versarial attribution and meet the requirements for different attribution granularities,attack algorithms and victim models were layered,and the dependencies between different levels were utilized.A multi-task adversarial attribu-tion method based on a hierarchical structure was proposed.This method simultaneously performed the attribution tasks of attack algorithms and victim models at different levels and employed hierarchical path prediction to learn the dependencies between these levels.Experimental results on multiple datasets demonstrate that the proposed method achieves better attribution performance compared to other attribution methods.关键词
深度学习/对抗样本归因/多任务学习/分层结构Key words
deep learning/adversarial attribution/multi-task learning/hierarchical structure分类
计算机与自动化引用本文复制引用
孙旭,张文琼,龙显忠,李云..基于分层结构的多任务对抗样本归因方法[J].网络与信息安全学报,2025,11(1):92-105,14.基金项目
国家自然科学基金(62476137) The National Natural Science Foundation of China(62476137) （62476137）
