Advbox: a toolbox to generate adversarial examples that fool neural networks

In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. Recent studies have shown that they are all vulnerable to the attack of adversarial...

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Bibliographic Details
Published in:arXiv.org 2020-08
Main Authors: Goodman, Dou, Hao Xin, Wang, Yang, Wu, Yuesheng, Xiong Junfeng, Zhang, Huan
Format: Article
Language:English
Subjects:
Algorithms
Computer vision
Face recognition
Human performance
Machine learning
Neural networks
Visual tasks
Online Access:Get full text
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Summary:In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. Recent studies have shown that they are all vulnerable to the attack of adversarial examples. Small and often imperceptible perturbations to the input images are sufficient to fool the most powerful neural networks. \emph{Advbox} is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle, PyTorch, Caffe2, MxNet, Keras, TensorFlow and it can benchmark the robustness of machine learning models. Compared to previous work, our platform supports black box attacks on Machine-Learning-as-a-service, as well as more attack scenarios, such as Face Recognition Attack, Stealth T-shirt, and DeepFake Face Detect. The code is licensed under the Apache 2.0 and is openly available at https://github.com/advboxes/AdvBox. Advbox now supports Python 3.
ISSN:2331-8422