  Given the great threat of adversarial attacks against Deep Neural Networks
(DNNs), numerous works have been proposed to boost transferability to attack
real-world applications. However, existing attacks often utilize advanced
gradient calculation or input transformation but ignore the white-box model.
Inspired by the fact that DNNs are over-parameterized for superior performance,
we propose diversifying the high-level features (DHF) for more transferable
adversarial examples. In particular, DHF perturbs the high-level features by
randomly transforming the high-level features and mixing them with the feature
of benign samples when calculating the gradient at each iteration. Due to the
redundancy of parameters, such transformation does not affect the
classification performance but helps identify the invariant features across
different models, leading to much better transferability. Empirical evaluations
on ImageNet dataset show that DHF could effectively improve the transferability
of existing momentum-based attacks. Incorporated into the input
transformation-based attacks, DHF generates more transferable adversarial
examples and outperforms the baselines with a clear margin when attacking
several defense models, showing its generalization to various attacks and high
effectiveness for boosting transferability. Code is available at
https://github.com/Trustworthy-AI-Group/DHF.
