计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211100074-5.doi: 10.11896/jsjkx.211100074
杨浩, 闫巧
YANG Hao, YAN Qiao
摘要: 验证码被广泛应用于网站、应用程序的注册登录环节以区分人类用户与计算机程序。然而随着深度学习的发展,许多针对验证码的深度学习识别方法不断被提出,验证码不再能较好地区分人类用户与计算机程序,验证码的安全性面临着极大挑战。对抗样本可以使神经网络的输出结果产生大幅误差,将对抗样本与验证码结合以抵御深度学习识别系统对验证码的攻击是一种行之有效的方法。将图像领域的对抗样本生成方法用于生成对抗验证码来防御深度学习方法是当前的研究热点之一。现有的字符对抗验证码生成方法都是需要知道攻击网络的结构参数信息的白盒方法,然而在实际的验证码应用场景中通常无法知道攻击网络的信息,健壮性的验证码应该在不知道攻击者信息的情况下依然有良好的防御能力。因此提出了一种基于差分进化算法的黑盒字符型对抗验证码生成方法(Adversarial Character CAPTCHA Generation Method Based on Differential Evolution Algorithm,ACoDE),在无需了解攻击网络信息的情况下通过优化经典差分进化算法变异过程中的缩放因子以及种群进化策略来提高算法的求解能力,使对抗样本误导神经网络的能力更强。将该对抗样本生成方法用于字符验证码数据集后目前最先进的基于卷积神经网络的字符型验证码识别系统的识别准确率降低到了30%以下,且对抗验证码的视觉效果比其他白盒方法生成的对抗验证码更好。
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