Turing　test　was　originally　proposed　to　examine　whether　machine＇s　behavior　is　indistinguishable　from　a　human.　The　most　popular　and　practical　Turing　test　is　CAPTCHA,　which　is　to　discriminate　algorithm　from　human　by　offering　recognition-alike　questions.　The　recent　development　of　deep　learning　has　significantly　advanced　the　capability　of　algorithm　in　solving　CAPTCHA　questions,　forcing　CAPTCHA　designers　to　increase　question　complexity.　Instead　of　designing　questions　difficult　for　both　algorithm　and　human,　this　study　attempts　to　employ　the　limitations　of　algorithm　to　design　robust　CAPTCHA　questions　easily　solvable　to　human.　Specifically,　our　data　analysis　observes　that　human　and　algorithm　demonstrates　different　vulnerability　to　visual　distortions:　adversarial　perturbation　is　significantly　annoying　to　algorithm　yet　friendly　to　human.　We　are　motivated　to　employ　adversarially　perturbed　images　for　robust　CAPTCHA　design　in　the　context　of　character-based　questions.　Four　modules　of　multi-target　attack,　ensemble　adversarial　training,　image　preprocessing　differentiable　approximation,　and　expectation　are　proposed　to　address　the　characteristics　of　character-based　CAPTCHA　cracking.　Qualitative　and　quantitative　experimental　results　demonstrate　the　effectiveness　of　the　proposed　solution.　We　hope　this　study　can　lead　to　the　discussions　around　adversarial　attack/defense　in　CAPTCHA　design　and　also　inspire　the　future　attempts　in　employing　algorithm　limitation　for　practical　usage.