Adversarial generative models, such as Generative Adversarial Networks
(GANs), are widely applied for generating various types of data, i.e., images,
text, and audio. Accordingly, its promising performance has led to the
GAN-based adversarial attack methods in the white-box and black-box attack
scenarios. The importance of transferable black-box attacks lies in their
ability to be effective across different models and settings, more closely
aligning with real-world applications. However, it remains challenging to
retain the performance in terms of transferable adversarial examples for such
methods. Meanwhile, we observe that some enhanced gradient-based transferable
adversarial attack algorithms require prolonged time for adversarial sample
generation. Thus, in this work, we propose a novel algorithm named GE-AdvGAN to
enhance the transferability of adversarial samples whilst improving the
algorithm's efficiency. The main approach is via optimising the training
process of the generator parameters. With the functional and characteristic
similarity analysis, we introduce a novel gradient editing (GE) mechanism and
verify its feasibility in generating transferable samples on various models.
Moreover, by exploring the frequency domain information to determine the
gradient editing direction, GE-AdvGAN can generate highly transferable
adversarial samples while minimizing the execution time in comparison to the
state-of-the-art transferable adversarial attack algorithms. The performance of
GE-AdvGAN is comprehensively evaluated by large-scale experiments on different
datasets, which results demonstrate the superiority of our algorithm. The code
for our algorithm is available at: this https URL

通过优化生成器参数的训练过程并引入梯度编辑机制，GE-AdvGAN 算法能够生成高度可迁移的对抗样本并在执行时间上与现有方法相比保持最小化。