Papers › Feature Space Perturbations Yield More Transferable Adversarial Examples
Feature Space Perturbations Yield More Transferable Adversarial Examples
Nathan Inkawhich, Wei Wen, Hai (Helen) Li, Yiran Chen
Many recent works have shown that deep learning models are vulnerable to quasi-imperceptible input perturbations, yet practitioners cannot fully explain this behavior. This work describes a transfer-based blackbox targeted adversarial attack of deep feature space representations that also provides insights into cross-model class representations of deep CNNs. The attack is explicitly designed for transferability and drives feature space representation of a source image at layer L towards the representation of a target image at L. The attack yields highly transferable targeted examples, which outperform competition winning methods by over 30% in targeted attack metrics. We also show the choice of L to generate examples from is important, transferability characteristics are blackbox model agnostic, and indicate that well trained deep models have similar highly-abstract representations.
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