889 citations · 1.7k across the 35 of their papers we have counts for
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Deep Neural Rejection against Adversarial Examples
Angelo Sotgiu, Ambra Demontis, Marco Melis +4
Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples t…
Towards Quality Assurance of Software Product Lines with Adversarial Configurations
Paul Temple, Mathieu Acher, Gilles Perrouin +3
Software product line (SPL) engineers put a lot of effort to ensure that, through the setting of a large number of possible configuration options, products are acceptable and well-…
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Francesco Crecchi, Davide Bacciu, Battista Biggio
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combin…
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries
Luca Demetrio, Battista Biggio, Giovanni Lagorio +2
Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that…