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 license and is openly available at https://github.com/advboxes/AdvBox.

近年来，神经网络被广泛应用于计算机视觉任务，然而最近的研究表明它们都容易受到对抗性样本的攻击。该研究提供了一种名为Advbox的工具箱来生成对抗性样本，并支持Python 3以在多个机器学习框架中使用，该工具箱还支持黑盒攻击和针对不同的攻击场景， 如人脸识别攻击，防护服攻击和DeepFake攻击的评估。

Advbox：生成欺骗神经网络的对抗样本工具箱