  Adversarial examples generated from surrogate models often possess the
ability to deceive other black-box models, a property known as transferability.
Recent research has focused on enhancing adversarial transferability, with
input transformation being one of the most effective approaches. However,
existing input transformation methods suffer from two issues. Firstly, certain
methods, such as the Scale-Invariant Method, employ exponentially decreasing
scale invariant parameters that decrease the adaptability in generating
effective adversarial examples across multiple scales. Secondly, most mixup
methods only linearly combine candidate images with the source image, leading
to reduced features blending effectiveness. To address these challenges, we
propose a framework called Uniform Scale and Mix Mask Method (US-MM) for
adversarial example generation. The Uniform Scale approach explores the upper
and lower boundaries of perturbation with a linear factor, minimizing the
negative impact of scale copies. The Mix Mask method introduces masks into the
mixing process in a nonlinear manner, significantly improving the effectiveness
of mixing strategies. Ablation experiments are conducted to validate the
effectiveness of each component in US-MM and explore the effect of
hyper-parameters. Empirical evaluations on standard ImageNet datasets
demonstrate that US-MM achieves an average of 7% better transfer attack success
rate compared to state-of-the-art methods.
