LOTS about attacking deep features

By Andras Rozsa, Manuel Günther, Terrance E. Boult. 2016

44 citations recorded by the source corpus.

Deep neural networks provide state-of-the-art performance on various tasks and are, therefore, widely used in real world applications. DNNs are becoming frequently utilized in biometrics for extracting deep features, which can be used in recognition systems for enrolling and recognizing new individuals. It was revealed that deep neural networks suffer from a fundamental problem, namely, they can unexpectedly misclassify examples formed by slightly perturbing correctly recognized inputs. Various approaches have been developed for generating these so-called adversarial examples, but they aim at attacking end-to-end networks. For biometrics, it is natural to ask whether systems using deep features are immune to or, at least, more resilient to attacks than end-to-end networks. In this paper, we introduce a general technique called the layerwise origin-target synthesis (LOTS) that can be efficiently used to form adversarial examples that mimic the deep features of the target. We analyze and compare the adversarial robustness of the end-to-end VGG Face network with systems that use Euclidean or cosine distance between gallery templates and extracted deep features. We demonstrate that iterative LOTS is very effective and show that systems utilizing deep features are easier to attack than the end-to-end network.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Backdooring Convolutional Neural Networks via Targeted Weight Perturbations (2018, background): “These are images that have been perturbed in some way that cause misclassifications when given as inputs to these networks [7, 21, 23, 25, 29, 22].”
Enhancing Adversarial Example Transferability With an Intermediate Level Attack (2019, background): “Two other related works [12, 24] focus on perturbing intermediate activation maps for the purpose of increasing adversarial transferability in a method similar to that of [32, 25] except they focus on black-box transferability.”