Deep　neural　networks　(DNNs)　are　vulnerable　to　adversarial　examples,　with　transfer　attacks　in　black-box　scenarios　posing　a　severe　real-world　threat.　Adversarial　perturbation　is　often　globally　manipulated　image　disturbances　crafted　in　the　spatial　domain,　leading　to　perceptible　noise　due　to　overfitting　to　the　source　model.　Both　the　human　visual　system　(HVS)　and　DNNs　(endeavoring　to　mimic　HVS　behavior)　exhibit　unequal　sensitivity　to　different　frequency　components　of　an　image.　In　this　paper,　we　intend　to　exploit　this　characteristic　to　create　frequency-aware　perturbation.　Concentrating　adversarial　perturbations　on　components　within　images　that　contribute　more　significantly　to　model　inference　to　enhance　the　performance　of　transfer　attacks.　We　devise　a　systematic　approach　to　select　and　constrain　adversarial　optimization　in　a　subset　of　frequency　components　that　are　more　critical　to　model　prediction.　Specifically,　we　measure　the　contributions　of　each　individual　frequency　component　and　devise　a　scheme　to　concentrate　adversarial　optimization　on　these　important　frequency　components,　thereby　creating　frequency-aware　perturbations.　Our　approach　confines　perturbations　within　model-agnostic　critical　frequency　components,　significantly　reducing　overfitting　to　the　source　model.　Our　approach　can　be　seamlessly　integrated　with　existing　state-of-the-art　attacks.　Experiments　demonstrate　that　while　concentrating　perturbation　within　selected　frequency　components　yields　a　smaller　perturbation　magnitude　overall,　our　approach　does　not　sacrifice　adversarial　effectiveness.　Conversely,　our　frequency-aware　perturbation　manifests　superior　performance,　boosting　imperceptibility,　transferability,　and　evasion　against　various　defenses.　IEEE