An empirical study of derivative-free-optimization algorithms for targeted black-box attacks in deep neural networks
We perform a comprehensive study on the performance of derivative free optimization (DFO) algorithms for the generation of targeted black-box adversarial attacks on Deep Neural Network (DNN) classifiers assuming the perturbation energy is bounded by an ℓ∞ constraint and the number of queries to the...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer
2021
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