Deep　neural　networks　are　especially　vulnerable　to　adversarial　example,　which　could　mislead　classifiers　by　adding　imperceptible　perturbations.　While　previous　researches　can　effec-Tively　generate　adversarial　example　in　the　white-box　environment,　it　is　a　challenge　to　produce　threatening　adversarial　example　in　the　black-box　environment　till　now,　where　attackers　only　have　access　to　obtain　the　predicts　of　models　to　inputs.　To　conquer　the　problem,　a　feasible　solution　is　harnessing　the　transferability　of　adversarial　examples　and　the　property　makes　adversarial　examples　can　successfully　attack　multiple　models　simultaneously.　Therefore,　this　paper　explores　the　way　to　enhance　transfer-Ability　of　adversarial　examples　and　then　propose　a　Nadam-based　iterative　algorithm　(NAI-FGM).　NAI-FGM　can　achieve　better　convergence　and　effectively　correct　the　deviation　so　as　to　boost　the　transferability　of　adversarial　examples.　To　validate　the　effectiveness　and　transferability　of　adversarial　examples　generated　by　our　proposed　NAI-FGM,　this　study　conducts　the　attacks　on　various　single　models　and　ensemble　models　on　open　Cifar-10　and　Cifar-100.　Experiment　results　exhibit　the　superiority　of　NAIFGM　that　achieves　higher　transferability　than　state-of-The-Art　methods　on　average　against　black-box　models.　©　COPYRIGHT　SPIE.　Downloading　of　the　abstract　is　permitted　for　personal　use　only.