Publications [#370057] of Lawrence Carin

Papers Published

1. Inkawhich, N; Liang, KJ; Carin, L; Chen, Y, TRANSFERABLE PERTURBATIONS OF DEEP FEATURE DISTRIBUTIONS, 8th International Conference on Learning Representations, ICLR 2020 (January, 2020)
   (last updated on 2024/12/31)

   Abstract:
   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 analysis 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.