Research on the avalanche effect of image encryption based on the Cycle-GAN

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Introduction

With the rapid advancement of artificial intelligence (AI) technology, the protection of patient medical image privacy and security has become a critical concern in current research on image privacy protection. However, traditional methods for encrypting medical images have faced criticism due to their limited flexibility and inadequate security. To overcome these limitations, this study proposes a novel chaotic medical image encryption method, called AT-ResNet-CM, which incorporates the attention mechanism fused with the ResNet model.

Methods

The proposed method utilizes the ResNet model as the underlying network for constructing the encryption and decryption framework. The ResNet's residual structure and jump connections are employed to effectively extract profound information from medical images and expedite the model's convergence. To enhance security, the output of the ResNet model is encrypted using a logistic chaotic system, introducing randomness and complexity to the encryption process. Additionally, an attention mechanism is introduced to enhance the model's response to the region of interest within the medical image, thereby strengthening the security of the encrypted network.

Results

Experimental simulations and analyses were conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method outperforms alternative models in terms of encryption effectiveness, as indicated by a horizontal correlation coefficient of 0.0021 and information entropy of 0.9887. Furthermore, the incorporation of the attention mechanism significantly improves the encryption performance, reducing the horizontal correlation coefficient to 0.0010 and increasing the information entropy to 0.9965. These findings validate the efficacy of the proposed method for medical image encryption tasks, as it offers enhanced security and flexibility compared to existing approaches.

Discussion

In conclusion, the AT-ResNet-CM method presents a promising solution to address the limitations of traditional encryption techniques in protecting patient medical images. By leveraging the attention mechanism fused with the ResNet model, the method achieves improved security and flexibility. The experimental results substantiate the superiority of the proposed method in terms of encryption effectiveness, horizontal correlation coefficient, and information entropy. The proposed method not only addresses the shortcomings of traditional methods but also provides a more robust and reliable approach for safeguarding patient medical image privacy and security.

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

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%.

FEDResNet: a flexible image encryption and decryption scheme based on end-to-end image diffusion with dilated ResNet

Image encryption has emerged as a method of disguising an image with a noisy or meaningless appearance to prevent its content from being accessed by unauthorized users. We propose an architecture named flexible image encryption and decryption ResNet (FEDResNet) for diffusing an image in end-to-end mode. The architecture consists of an encryption network for diffusing the image and a decryption network for restoring the plaintext image from the diffused image. To enhance the security of the encrypted image, the diffused image is further processed with two optional operations: parallel scrambling and serial diffusion. Two key planes are constructed based on a user-defined key with a chaotic map to control the authority to access images. The structure and parameters of FEDResNet can be shared publicly by different users; hence, it is more flexible and convenient than previous deep-learning-based image encryption methods. A classification network is trained to classify medical images in ciphertext environments. The proposed FEDResNet is trained and tested on the ImageNet data set. Extensive experiments have been performed, and the experimental results suggest that the proposed model can achieve a high level of security with satisfactory efficiency. The experimental results also show that FEDResNet-encrypted images can be classified directly in the ciphertext domain by authorized users as accurately as plaintext images, which is a superior property that is not possessed by traditional image encryption methods.