Deep Learning and Music Adversaries

Kereliuk, Corey; Sturm, Bob L.; Larsen, Jan Petter

doi:10.48550/arxiv.1507.04761

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2018

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“…In [9] adversarial examples were generated for music data. The authors report that back-feeding of adversarial examples to the training set did not result in an improved resistance to adversarial examples.…”

Section: Related Workmentioning

confidence: 99%

Understanding adversarial training: Increasing local stability of supervised models through robust optimization

2018

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We propose a general framework for increasing local stability of Artificial Neural Nets (ANNs) using Robust Optimization (RO). We achieve this through an alternating minimization-maximization procedure, in which the loss of the network is minimized over perturbed examples that are generated at each parameter update. We show that adversarial training of ANNs is in fact robustification of the network optimization, and that our proposed framework generalizes previous approaches for increasing local stability of ANNs. Experimental results reveal that our approach increases the robustness of the network to existing adversarial examples, while making it harder to generate new ones. Furthermore, our algorithm improves the accuracy of the network also on the original test data.

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Section: Related Workmentioning

confidence: 99%