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See detailEfficient and Transferable Adversarial Examples from Bayesian Neural Networks
Gubri, Martin UL; Cordy, Maxime UL; Papadakis, Mike UL et al

E-print/Working paper (2021)

An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on a surrogate ensemble model to increase diversity. We argue that transferability is ... [more ▼]

An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on a surrogate ensemble model to increase diversity. We argue that transferability is fundamentally related to epistemic uncertainty. Based on a state-of-the-art Bayesian Deep Learning technique, we propose a new method to efficiently build a surrogate by sampling approximately from the posterior distribution of neural network weights, which represents the belief about the value of each parameter. Our extensive experiments on ImageNet and CIFAR-10 show that our approach improves the transfer rates of four state-of-the-art attacks significantly (up to 62.1 percentage points), in both intra-architecture and inter-architecture cases. On ImageNet, our approach can reach 94% of transfer rate while reducing training computations from 11.6 to 2.4 exaflops, compared to an ensemble of independently trained DNNs. Our vanilla surrogate achieves 87.5% of the time higher transferability than 3 test-time techniques designed for this purpose. Our work demonstrates that the way to train a surrogate has been overlooked although it is an important element of transfer-based attacks. We are, therefore, the first to review the effectiveness of several training methods in increasing transferability. We provide new directions to better understand the transferability phenomenon and offer a simple but strong baseline for future work. [less ▲]

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See detailSearch-based adversarial testing and improvement of constrained credit scoring systems
Ghamizi, Salah UL; Cordy, Maxime UL; Gubri, Martin UL et al

in ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE '20), November 8-13, 2020 (2020)

Detailed reference viewed: 125 (24 UL)