For adversarial attacks， black-box attacks are more challenging and applicable than white-box attacks. Recently， black-box attacks based on the transferability of adversarial examples have become mainstream methods. However， the adversarial examples generated by most existing methods exhibit low efficiency in black-box attacks. In this paper， a combination strategy based on Gaussian noise and flipping is proposed to enhance the transferability of adversarial examples， thus achieving higher black-box attack success rates. Moreover， this strategy can be integrated into any gradient-based method to obtain stronger attacks. Extensive experiments on an ImageNet-compatible dataset show that our proposed method can generate more transferable adversarial examples. In addition， our best attack can fool six state-of-the-art defense models with an average success rate of 86.2%， and deliver 8.0% success rate increasement compared with the state-of-the-art gradient-based attack.