Deep learning models are susceptible to adversarial attacks, where slight
perturbations to input data lead to misclassification. Adversarial attacks
become increasingly effective with access to information about the targeted
classifier. In the context of multi-task learning, where a single model learns
multiple tasks simultaneously, attackers may aim to exploit vulnerabilities in
specific tasks with limited information. This paper investigates the
feasibility of attacking hidden tasks within multi-task classifiers, where
model access regarding the hidden target task and labeled data for the hidden
target task are not available, but model access regarding the non-target tasks
is available. We propose a novel adversarial attack method that leverages
knowledge from non-target tasks and the shared backbone network of the
multi-task model to force the model to forget knowledge related to the target
task. Experimental results on CelebA and DeepFashion datasets demonstrate the
effectiveness of our method in degrading the accuracy of hidden tasks while
preserving the performance of visible tasks, contributing to the understanding
of adversarial vulnerabilities in multi-task classifiers.

该研究论文调查了针对多任务分类器中隐藏任务的攻击的可行性，提出了一种新颖的敌对攻击方法，利用非目标任务的知识和多任务模型的共享主干网络迫使模型遗忘与目标任务相关的知识，实验证明了该方法在削弱隐藏任务的准确性方面的有效性，并且保留了可见任务的性能，有助于了解多任务分类器中的敌对性漏洞。