Adversarial　perturbation　for　Automatic　Speaker　Verification　(ASV)　systems　has　provided　great　opportunity　to　ensure　the　trustworthiness　and　continuous　improvement　of　voice-based　authentication　technologies　in　the　face　of　evolving　security　threats.　The　technology　remains　challenging　due　to　the　high　requirement　of　attacks　that　are　both　effective　and　stealthy　while　navigating　the　increasing　sophistication　of　system　defences　and　voice　data.　This　paper　proposes　a　novel　method　called　GFE-PGDI　for　the　general　task　of　adversarial　perturbation　generation　by　integrating　global　feature　extraction　and　PGD-based　optimization.　Specifically,　it　relies　on　a　Mel-Frequency　Cepstral　Coefficients　(MFCC)　strategy　to　perform　global　feature　extraction　from　the　input　audio　and　uses　Projected　Gradient　Descent　(PGD)　to　maximize　the　effectiveness　of　the　attack　by　calculating　the　cosine　similarity　between　the　feature　matrix　and　the　target　speaker＇s　voiceprint　matrix.　It　also　applies　a　customized　Carlini　and　Wagner　(CW)　function　attack　to　minimize　the　impact　on　ASV,　generating　system-independent　and　text-independent　perturbations.　Experimental　evaluations　show　that　the　perturbations　can　effectively　mislead　speaker　verification　systems　in　different　environments　with　a　success　rate　of　over　95%,　further　validating　the　effectiveness　of　the　GFE-PGDI.　©2025　IEEE.