Adversarial example soups: averaging multiple adversarial examples improves transferability without increasing additional generation time

For transfer-based attacks, the adversarial examples are crafted on the surrogate model, which can be implemented to mislead the target model effectively. The conventional method for maximizing adversarial transferability involves: (1) fine-tuning hyperparameters to generate multiple batches of adversarial examples on the substitute model; (2) conserving the batch of adversarial examples that have the best comprehensive performance on substitute model and target model, and discarding the others. In this work, we revisit the second step of this process in the context of fine-tuning hyperparameters to craft adversarial examples, where multiple batches of fine-tuned adversarial examples often appear in a single high error hilltop. We demonstrate that averaging multiple batches of adversarial examples under different hyperparameter configurations, which refers to as "adversarial example soups", can often enhance adversarial transferability. Compared with traditional methods, the proposed method incurs no additional generation time and computational cost. Besides, our method is orthogonal to existing transfer-based methods and can be combined with them seamlessly to generate more transferable adversarial examples. Extensive experiments on the ImageNet dataset show that our methods achieve a higher attack success rate than the state-of-the-art attacks.