  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.
