Image encryption based on adversarial neural cryptography and SHA controlled chaos

Wu, Jianhua; Xia, Weixia; Zhu, Gailin; Liu, Hai; Ma, Lujuan; Xiong, Jianping

doi:10.1080/09500340.2021.1900440

Cited by 19 publications

(12 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kolosnjaji et al [17] used CNNs and RNNs to identify malware. The list of call sequences to the API kernel is converted into binary vectors using one-hot encoding.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

Chinthamu,

Gooda,

Venkatachalam

et al. 2023

IJRITCC

View full text Add to dashboard Cite

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks.

show abstract

“…Kolosnjaji et al [17] used CNNs and RNNs to identify malware. The list of call sequences to the API kernel is converted into binary vectors using one-hot encoding.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

Chinthamu,

Gooda,

Venkatachalam

et al. 2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…This encryption process is depicted in Figure 4 . Wu et al ( 2021 ) introduced an image encryption method based on adversarial neural cryptography (ANC) and SHA control chaos. This approach obtains an intermediate image similar to noise by training a GAN, and subsequently performs an XOR operation based on the Logistic mapping on the intermediate image to generate the final ciphertext.…”

Section: Related Researchmentioning

confidence: 99%

“…To highlight the ResNet-CM model has better medical image encryption effect than other models, we carried out comparative experiments between ResNet-CM model and Zhang et al ( 2012 ), Ding et al ( 2020 ), Yang et al ( 2020 ), Hua et al ( 2021 ), Wu et al ( 2021 ), Wang et al ( 2022 ), and Zhu et al ( 2022 ). To better measure the medical image encryption effect of these models, the horizontal correlation coefficient and information entropy of these models on encrypted images are shown in Table 4 .…”

Section: Experimentationmentioning

confidence: 99%

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Peng

2023

Front. Neurosci.

View full text Add to dashboard Cite

IntroductionWith 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.MethodsThe 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.ResultsExperimental 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.DiscussionIn 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.

show abstract

“…The parties generate their own TPMs using common parameters and starting random weight values for the TPMs in the standard neural cryptography [16] . After that, they create a random input vector and compute their own output values by feeding the TPM with the produced common input.…”

Section: Introductionmentioning

confidence: 99%

MAN C: A masked autoencoder neural cryptography based encryption scheme for CT scan images

Kumar,

Tanwar,

Kumar

2024

MethodsX

View full text Add to dashboard Cite

Image encryption based on adversarial neural cryptography and SHA controlled chaos

Cited by 19 publications

References 37 publications

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

Chaotic medical image encryption method using attention mechanism fusion ResNet model

MAN C: A masked autoencoder neural cryptography based encryption scheme for CT scan images

Contact Info

Product

Resources

About