Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map

Abstract

Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of counterfeiting, the iris method has become an essential research object in data security research. This study proposes a revolutionary face feature encryption technique that combines picture optimization with cryptography and deep learning (DL) architectures. To improve the security of the key, an optical chaotic map is employed to manage the initial standards of the 5D conservative chaotic method. A safe Crypto General Adversarial neural network and chaotic optical map are provided to finish the course of encrypting and decrypting facial images. The target field is used as a "hidden factor" in the machine learning (ML) method in the encryption method. An encrypted image is recovered to a unique image using a modernization network to achieve picture decryption. A region-of-interest (ROI) network is provided to extract involved items from encrypted images to make data mining easier in a privacy-protected setting. This study’s findings reveal that the recommended implementation provides significantly improved security without sacrificing image quality. Experimental results show that the proposed model outperforms the existing models in terms of PSNR of 92%, RMSE of 85%, SSIM of 68%, MAP of 52%, and encryption speed of 88%.

Advances and Vulnerabilities in Modern Cryptographic Techniques: A Comprehensive Survey on Cybersecurity in the Domain of Machine/Deep Learning and Quantum Techniques

“…In [170], Alsafyani et al proposed an advanced face feature encryption method that combines image optimization, cryptography, and deep learning. Their approach integrates an optical chaotic map to bolster key security within a 5D conservative chaotic method.…”

Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection

“…Compared to traditional image quality measures, SSIM is more in line with the human eye's judgement of images. Given two signals x and y, the structural similarity between the two is defined [37,38,39,40,41] as shown in Equation 5and Equation 6.…”

Local bit-level image encryption algorithm based on one dimensional zero excluded chaotic map

“…To enhance the security and efficiency of chaos-based image encryption algorithms, researchers have put forth various methodologies [17,18]. This has led to the integration of chaotic image encryption with diverse technologies, including matrix semi-tensor products [19,20], Boolean networks [21,22], neural networks [23][24][25], DNA encoding [26][27][28], elliptic curves [29,30], and quantum encryption 2. Designed chaotic system 2.1.…”