Improving Transferability of Adversarial Examples via Bayesian Attacks
Qizhang Li Yiwen Guo Xiaochen Yang Wangmeng Zuo Hao Chen
This paper presents a substantial extension of our work published at ICLR.
Our ICLR work advocated for enhancing transferability in adversarial examples
by incorporating a Bayesian formulation into model parameters, which
effectively emulates the ensemble of infinitely many deep neural networks,
while, in this paper, we introduce a novel extension by incorporating the
Bayesian formulation into the model input as well, enabling the joint
diversification of both the model input and model parameters. Our empirical
findings demonstrate that: 1) the combination of Bayesian formulations for both
the model input and model parameters yields significant improvements in
transferability; 2) by introducing advanced approximations of the posterior
distribution over the model input, adversarial transferability achieves further
enhancement, surpassing all state-of-the-arts when attacking without model
fine-tuning. Moreover, we propose a principled approach to fine-tune model
parameters in such an extended Bayesian formulation. The derived optimization
objective inherently encourages flat minima in the parameter space and input
space. Extensive experiments demonstrate that our method achieves a new
state-of-the-art on transfer-based attacks, improving the average success rate
on ImageNet and CIFAR-10 by 19.14% and 2.08%, respectively, when comparing with
our ICLR basic Bayesian method. We will make our code publicly available.