Several　biometric　privacy-enhancing　techniques　have　been　appraised　to　protect　face　image　privacy.　However,　a　face　privacy　protection　algorithm　is　usually　designed　for　a　specific　face　recognition　algorithm.　When　the　structure　or　threshold　of　the　face　recognition　algorithm　is　fine-tuned,　the　protection　algorithm　may　be　invalid.　It　will　cause　the　network　bloated　and　make　the　image　distortion　target　multiple　FRAs　through　the　existing　technology　simultaneously.　To　address　this　problem,　a　fusion　technology　is　developed　to　cope　with　the　changeable　face　recognition　algorithms　via　an　image　perturbation　method.　The　image　perturbation　is　performed　by　using　a　GAN-improved　algorithm　including　generator,　nozzles　and　validator,　referred　to　as　the　Adversarial　Fusion　algorithm.　A　nozzle　structure　is　proposed　to　replace　the　discriminator.　Paralleling　multiple　face　recognition　algorithms　on　the　nozzle　can　improve　the　compatibility　of　the　generated　image.　Next,　a　validator　is　added　to　the　training　network,　which　takes　part　in　the　inverse　back　coupling　of　the　generator.　This　component　can　make　the　generated　graphics　have　no　impact　on　human　vision.　Furthermore,　the　group　hunting　theory　is　quoted　to　make　the　network　stable　and　up　to　4.8　times　faster　than　other　models　in　training.　The　experimental　results　show　that　the　Adversarial　Fusion　algorithm　can　not　only　change　the　image　feature　distribution　by　over　42%　but　also　deal　with　at　least　5　commercial　face　recognition　algorithms　at　the　same　time.