School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
School of Software and Microelectronics, Peking University, 24th Jinyuan Road, Daxing Industrial District, Beijing 102600, China.
Comput Intell Neurosci. 2022 Aug 5;2022:9576184. doi: 10.1155/2022/9576184. eCollection 2022.
With the continuous development of Internet technology and technological innovation, image recognition technologies such as face unlocking and face brushing payment have gradually entered daily life. However, it can not be ignored that these technologies not only bring us great convenience but also face great risks. The biological characteristics of a face image are unique, and it will be difficult to modify once it is leaked. If the image information stored in the cloud is leaked because it cannot be properly kept, users have no privacy. The encryption and recognition of face image can effectively solve this problem. Aiming at this, high-dimensional chaos Henon Map and one-dimensional chaos Logistic Map are used to generate a key to complete the encryption of the image in the transformation domain, and the capacity and complexity of the key are further enhanced. Then, combined with BP neural network to achieve face image recognition. Finally, the robustness of the proposed algorithm is verified and analyzed by conventional attacks, geometric attacks, and occlusion attacks.
随着互联网技术的不断发展和技术创新，人脸识别解锁和刷脸支付等图像识别技术逐渐走入日常生活。然而，不容忽视的是，这些技术不仅给我们带来了极大的便利，也面临着巨大的风险。人脸图像的生物特征是独一无二的，一旦泄露，很难修改。如果存储在云端的图像信息因无法妥善保存而泄露，用户将毫无隐私可言。人脸图像的加密和识别可以有效地解决这个问题。针对这一点，本研究使用高维混沌 Henon 映射和一维混沌 Logistic 映射生成密钥，以完成变换域中图像的加密，并进一步增强密钥的容量和复杂度。然后，结合 BP 神经网络实现人脸图像识别。最后，通过常规攻击、几何攻击和遮挡攻击验证和分析了所提出算法的鲁棒性。
