Boosting Adversarial Transferability across Model Genus by
  Deformation-Constrained Warping
Qinliang Lin Cheng Luo Zenghao Niu Xilin He Weicheng Xie Yuanbo Hou Linlin Shen Siyang Song
Adversarial examples generated by a surrogate model typically exhibit limited
transferability to unknown target systems. To address this problem, many
transferability enhancement approaches (e.g., input transformation and model
augmentation) have been proposed. However, they show poor performances in
attacking systems having different model genera from the surrogate model. In
this paper, we propose a novel and generic attacking strategy, called
Deformation-Constrained Warping Attack (DeCoWA), that can be effectively
applied to cross model genus attack. Specifically, DeCoWA firstly augments
input examples via an elastic deformation, namely Deformation-Constrained
Warping (DeCoW), to obtain rich local details of the augmented input. To avoid
severe distortion of global semantics led by random deformation, DeCoW further
constrains the strength and direction of the warping transformation by a novel
adaptive control strategy. Extensive experiments demonstrate that the
transferable examples crafted by our DeCoWA on CNN surrogates can significantly
hinder the performance of Transformers (and vice versa) on various tasks,
including image classification, video action recognition, and audio
recognition. Code is made available at https://github.com/LinQinLiang/DeCoWA.