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Deep learning models have been used for a wide variety of tasks. They areprevalent in computer vision, natural language processing, speech recognition,and other areas. While these models have worked well under many scenarios, ithas been shown that they are vulnerable to adversarial attacks. This has led toa proliferation of research into ways that such attacks could be identifiedand/or defended against. Our goal is to explore the contribution that can beattributed to using multiple underlying models for the purpose of adversarialinstance detection. Our paper describes two approaches that incorporaterepresentations from multiple models for detecting adversarial examples. Wedevise controlled experiments for measuring the detection impact ofincrementally utilizing additional models. For many of the scenarios weconsider, the results show that performance increases with the number ofunderlying models used for extracting representations.

Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances