Transferable Perturbations of Deep Feature Distributions [article]

Nathan Inkawhich, Kevin J Liang, Lawrence Carin, Yiran Chen
2020 arXiv   pre-print
Almost all current adversarial attacks of CNN classifiers rely on information derived from the output layer of the network. This work presents a new adversarial attack based on the modeling and exploitation of class-wise and layer-wise deep feature distributions. We achieve state-of-the-art targeted blackbox transfer-based attack results for undefended ImageNet models. Further, we place a priority on explainability and interpretability of the attacking process. Our methodology affords an
more » ... s of how adversarial attacks change the intermediate feature distributions of CNNs, as well as a measure of layer-wise and class-wise feature distributional separability/entanglement. We also conceptualize a transition from task/data-specific to model-specific features within a CNN architecture that directly impacts the transferability of adversarial examples.
arXiv:2004.12519v1 fatcat:aaz7x6jbe5duznlope46ub4ibq