Boosting Adversarial Transferability Through Adversarial Attack Enhancer

Adversarial attacks against deep learning models achieve high performance in white-box settings but often exhibit low transferability in black-box scenarios, especially against defended models. In this work, we propose Multi-Path Random Restart (MPRR), which initializes multiple restart points with random noise to optimize gradient updates and improve transferability. Building upon MPRR, we propose the Channel Shuffled Attack Method (CSAM), a new gradient-based attack that generates highly transferable adversarial examples via channel permutation of input images. Extensive experiments on the ImageNet dataset show that MPRR substantially improves the success rates of existing attacks (e.g., boosting FGSM, MI-FGSM, DIM, and TIM by 22.4–38.6%), and CSAM achieves average success rates 13.8–24.0% higher than state-of-the-art methods.