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PICTURE present at INNS/IEEE International Joint Conference on Neural Networks, IJCNN 2021

PICTURE consortium is pleased to participate in the International Joint Conference on Neural Networks 2021 with a paper from Rémi Bernhard (CEA LETI), Pierre-Alain Moellic (CEA LETI) and Jean-Max Dutertre (Mines Saint-Etienne)
Published on 3 May 2021

A work from CEA LETI and Mines Saint-Etienne has been accepted at the INNS/IEEE International Joint Conference on Neural Networks (IJCNN'21).

Rémi Bernhard, Pierre-Alain Moëllic, Jean-Max Dutertre, Luring Transferable Adversarial Perturbations for Deep Neural Networks, to appear in INNS/IEEE IJCNN 2021.

ARXIV Version

AbstractThe growing interest for adversarial examples, i.e. maliciously modified examples which fool a classifier, has resulted in many defenses intended to detect them, render them inoffensive or make the model more robust against them. In this paper, we pave the way towards a new approach to improve the robustness of a model against black-box transfer attacks.  A removable additional neural network is included in the target model, and is designed to induce the \textit{luring effect}, which tricks the adversary into choosing false directions to fool the target model.  Training the additional model is achieved thanks to a loss function acting on the logits sequence order. Our deception-based method only needs to have access to the predictions of the target model and does not require a labeled data set. We explain the luring effect thanks to the notion of robust and non-robust useful features and perform experiments on MNIST, SVHN and CIFAR10 to characterize and evaluate this phenomenon. Additionally, we scale the luring effect to ImageNet, experiment practical use of it and discuss its complementarity with other defense schemes.

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