  Deep learning models, even the-state-of-the-art ones, are highly vulnerable
to adversarial examples. Adversarial training is one of the most efficient
methods to improve the model's robustness. The key factor for the success of
adversarial training is the capability to generate qualified and divergent
adversarial examples which satisfy some objectives/goals (e.g., finding
adversarial examples that maximize the model losses for simultaneously
attacking multiple models). Therefore, multi-objective optimization (MOO) is a
natural tool for adversarial example generation to achieve multiple
objectives/goals simultaneously. However, we observe that a naive application
of MOO tends to maximize all objectives/goals equally, without caring if an
objective/goal has been achieved yet. This leads to useless effort to further
improve the goal-achieved tasks, while putting less focus on the
goal-unachieved tasks. In this paper, we propose \emph{Task Oriented MOO} to
address this issue, in the context where we can explicitly define the goal
achievement for a task. Our principle is to only maintain the goal-achieved
tasks, while letting the optimizer spend more effort on improving the
goal-unachieved tasks. We conduct comprehensive experiments for our Task
Oriented MOO on various adversarial example generation schemes. The
experimental results firmly demonstrate the merit of our proposed approach. Our
code is available at \url{https://github.com/tuananhbui89/TAMOO}.
