Stealthy Multi-task Adversarial Attacks

Deep neural networks are highly vulnerable to adversarialperturbations, raising serious safety concerns in the real-world systems.While prior work mainly explores single-task attacks or jointly degradingall tasks in multi-task models, practical scenarios often demand moreselective and stealthy attack strategies. To address this challenge, wepropose Stealthy Multi-Task Adversarial Attack (SMTA2 ), a novelframework that selectively degrades a targeted task while strictly pre-serving the performance of non-targeted tasks. We formulate this objec-tive as a constrained multi-objective optimization problem and designtask-aware adversarial perturbations that maximize degradation on thetargeted task without causing collateral damage on non-targeted tasks.To enhance practicality, we further introduce an automated loss-weighttuning strategy that dynamically balances attack and preservation objec-tives. Experiments on two multi-task benchmarks NYUv2 and Cityscapesdemonstrate that SMTA2 achieves strong attack performance on tar-geted tasks while maintaining non-targeted tasks intact on both unde-fended and adversarially trained models, establishing the first systematicframework for stealthy and selective multi-task attack framework.