A visually meaningful image encryption scheme based on a 5D chaotic map and deep learning

ABSTRACT

The VMIE techniques follow two steps: pre-encryption and embedding. In the first phase of the proposed method, the 5D chaotic map and zig-zag transformation are used for encryption, which provides higher security. In the second phase, the U-Net architecture-based encoder is implemented to hide the encrypted image in the reference image. Furthermore, an efficient decoder is designed to extract the encrypted image from the reference image. The exquisiteness of deep learning in image embedding is that it hides an image into the same size reference image without degrading the image quality. Moreover, to validate the proposed method, some standard security and image quality-related parameters are analysed. The results in terms of image security and quality parameters compared to the existing VMIE reveal that the proposed method is highly secure and efficient for imperceptible image communication and better image quality at the receiver’s end.

KEYWORDS:

• Visually meaningful image encryption
• image embedding
• information security
• deep learning
• 5D chaotic map
• image encryption
• U-Net
• steganography

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The image dataset used for training the model is publicly available at http://vis-www.cs.umass.edu/lfw/

Additional information

Notes on contributors

Varsha Himthani

Varsha Himthani received a bachelor's degree in information technology from Rajeev Gandhi Prodyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India, and a master's degree in computer science from Rajasthan Technical University, Rajasthan, India. She is pursuing Ph.D. in computer engineering at Manipal University Jaipur, Rajasthan. Since 2008, she has been an academician in the same field. Her research interests include information security and artificial intelligence.

Vijaypal Singh Dhaka

Vijaypal Singh Dhaka received the Ph.D. degree in computer science from Dr Bhimrao Ambedkar University, Agra, India, in 2010. He is a seasoned academician with a flair for entrepreneurial spirit. He enjoys a persistent passion for continuous learning for self and students' growth. He has more than 17 years of experience in the software industry, academics, research, teaching, and training. He has more than 90 publications in journals of great repute in his name and guided 14 research scholars to earn Ph.D. He has published more than 8 patents and acquired more than 20 copyrights on software applications and inventions. He received the “World Eminence Awards 2017” for Leading Research Contribution in ICT in 2016, at WS-4 in London in February 2017.

Manjit Kaur

Manjit Kaur (Senior Member, IEEE) received the M.E. degree in information technology from Punjab University, Chandigarh, India, in 2011, and the Ph.D. degree from the Thapar Institute of Engineering and Technology, Patiala, India, in 2019. She was an Assistant Professor at Chandigarh University, Mohali, India; Manipal University Jaipur, Jaipur, India; and Bennett University, Greater Noida, India. In 2021, she joined the School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea, where she is currently affiliated. Her research interests include wireless sensor networks, digital image processing, and metaheuristic techniques. She was in the top 2% list issues by “World Ranking of Top 2% Scientists” in 2021. She was part of the 11 Web of Science/Scopus indexed conferences.