172,613 research outputs found
Detecting adversarial manipulation using inductive Venn-ABERS predictors
Inductive Venn-ABERS predictors (IVAPs) are a type of probabilistic predictors with the theoretical guarantee that their predictions are perfectly calibrated. In this paper, we propose to exploit this calibration property for the detection of adversarial examples in binary classification tasks. By rejecting predictions if the uncertainty of the IVAP is too high, we obtain an algorithm that is both accurate on the original test set and resistant to adversarial examples. This robustness is observed on adversarials for the underlying model as well as adversarials that were generated by taking the IVAP into account. The method appears to offer competitive robustness compared to the state-of-the-art in adversarial defense yet it is computationally much more tractable
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense
Present attack methods can make state-of-the-art classification systems based
on deep neural networks misclassify every adversarially modified test example.
The design of general defense strategies against a wide range of such attacks
still remains a challenging problem. In this paper, we draw inspiration from
the fields of cybersecurity and multi-agent systems and propose to leverage the
concept of Moving Target Defense (MTD) in designing a meta-defense for
'boosting' the robustness of an ensemble of deep neural networks (DNNs) for
visual classification tasks against such adversarial attacks. To classify an
input image, a trained network is picked randomly from this set of networks by
formulating the interaction between a Defender (who hosts the classification
networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg
Game (BSG). We empirically show that this approach, MTDeep, reduces
misclassification on perturbed images in various datasets such as MNIST,
FashionMNIST, and ImageNet while maintaining high classification accuracy on
legitimate test images. We then demonstrate that our framework, being the first
meta-defense technique, can be used in conjunction with any existing defense
mechanism to provide more resilience against adversarial attacks that can be
afforded by these defense mechanisms. Lastly, to quantify the increase in
robustness of an ensemble-based classification system when we use MTDeep, we
analyze the properties of a set of DNNs and introduce the concept of
differential immunity that formalizes the notion of attack transferability.Comment: Accepted to the Conference on Decision and Game Theory for Security
(GameSec), 201
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