Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain

30 Jan 2020Yigit AlparslanJeremy Keim-ShenkShweta KhadeRachel Greenstadt

Numerous recent studies have demonstrated how Deep Neural Network (DNN) classifiers can be fooled by adversarial examples, in which an attacker adds perturbations to an original sample, causing the classifier to misclassify the sample. Adversarial attacks that render DNNs vulnerable in real life represent a serious threat, given the consequences of improperly functioning autonomous vehicles, malware filters, or biometric authentication systems... (read more)

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