关键词: 深度学习, 对抗样本, 通用对抗扰动, 图像验证码, 卷积神经网络, 图像分类

Abstract:

The development of convolutional neural networks makes image-based CAPTCHA no longer safe. Based on the universal adversarial perturbation in convolutional neural networks, an image-based CAPTCHA’s protection method is proposed. An algorithm is proposed for quickly generating universal adversarial perturbations by aggregating adversarial perturbation vectors that have similar orientation. Then, an image-based CAPTCHA’s protection scheme is designed by adding universal adversarial perturbations to the images to protect CAPTCHA from recognizing by convolutional neural networks. Experimental results on the ImageNet dataset show that the scheme has a lower cracking rate than DeepCAPTCHA, and can protect the image-based CAPTCHA from being cracked by state-of-the-art convolutional neural networks.

Key words: deep learning, adversarial example, universal adversarial perturbation, image-based CAPTCHA, convolutional neural network, image classification