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Poster
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
Antonio Barbalau · Adrian Cosma · Radu Tudor Ionescu · Marius Popescu

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1657

We study the task of replicating the functionality of black-box neural models, for which we only know the output class probabilities provided for a set of input images. We assume back-propagation through the black-box model is not possible and its training images are not available, e.g. the model could be exposed only through an API. In this context, we present a teacher-student framework that can distill the black-box (teacher) model into a student model with minimal accuracy loss. To generate useful data samples for training the student, our framework (i) learns to generate images on a proxy data set (with images and classes different from those used to train the black-box) and (ii) applies an evolutionary strategy to make sure that each generated data sample exhibits a high response for a specific class when given as input to the black box. Our framework is compared with several baseline and state-of-the-art methods on three benchmark data sets. The empirical evidence indicates that our model is superior to the considered baselines. Although our method does not back-propagate through the black-box network, it generally surpasses state-of-the-art methods that regard the teacher as a glass-box model. Our code is available at: https://github.com/antoniobarbalau/black-box-ripper.

Author Information

Antonio Barbalau (University of Bucharest)
Adrian Cosma (Politehnica University of Bucharest)
Radu Tudor Ionescu (University of Bucharest)
Marius Popescu (University of Bucharest)

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