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On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses
Neural networks are known to be vulnerable to adversarial examples. In t...
04/10/2018 ∙ by Anish Athalye, et al. ∙
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IS-CAM: Integrated Score-CAM for axiomatic-based explanations
Convolutional Neural Networks have been known as black-box models as hum...
10/06/2020 ∙ by Rakshit Naidu, et al. ∙
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Don't Paint It Black: White-Box Explanations for Deep Learning in Computer Security
Deep learning is increasingly used as a basic building block of security...
06/05/2019 ∙ by Alexander Warnecke, et al. ∙
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Camera Model Anonymisation with Augmented cGANs
The model of camera that was used to capture a particular photographic i...
02/18/2020 ∙ by Jerone T. A. Andrews, et al. ∙
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On Baselines for Local Feature Attributions
High-performing predictive models, such as neural nets, usually operate ...
01/04/2021 ∙ by Johannes Haug, et al. ∙
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RISE: Randomized Input Sampling for Explanation of Black-box Models
Deep neural networks are increasingly being used to automate data analys...
06/19/2018 ∙ by Vitali Petsiuk, et al. ∙
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Black Box to White Box: Discover Model Characteristics Based on Strategic Probing
In Machine Learning, White Box Adversarial Attacks rely on knowing under...
09/07/2020 ∙ by Josh Kalin, et al. ∙
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Evaluating Attribution Methods using White-Box LSTMs
10/16/2020 ∙ by Yiding Hao, et al. ∙
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Interpretability methods for neural networks are difficult to evaluate because we do not understand the black-box models typically used to test them. This paper proposes a framework in which interpretability methods are evaluated using manually constructed networks, which we call white-box networks, whose behavior is understood a priori. We evaluate five methods for producing attribution heatmaps by applying them to white-box LSTM classifiers for tasks based on formal languages. Although our white-box classifiers solve their tasks perfectly and transparently, we find that all five attribution methods fail to produce the expected model explanations.
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