https://doi.org/10.1140/epjs/s11734-025-01950-z
Regular Article
A neural network-guided chaos-based parallel encryption scheme for securing regions of interest in multiple images
1
School of Computer Science and Engineering, Northeastern University, 110819, Shenyang, China
2
Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, China
3
Maxio Technology (Hangzhou) Co., LTD, 6F, Building C, No.459 Qianmo Road, Juguang Center, Binjiang District, 310051, Hangzhou, China
4
School of Software, Dalian University of Technology, 116621, Dalian, China
a
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Received:
5
May
2025
Accepted:
7
September
2025
Published online:
16
September
2025
Abstract
In most images, the sensitive information is predominantly located within the region of interest (ROI). When dealing with large numbers of images, encrypting the entire image dataset can be time-consuming. By focusing encryption solely on the ROIs, we can efficiently protect confidential information while maintaining computational efficiency. In this paper, we propose a neural network-guided chaos-based parallel encryption scheme for securing regions of interest in multiple images. First, salient regions across multiple images are identified through salient object detection and subsequently converted into ROIs. The ROI pixels are then extracted and transformed into multiple pixel cubes, which are evenly distributed across multiple threads for concurrent encryption. Experimental results and security analysis demonstrate that our algorithm can efficiently protect ROI regions while maintaining high performance.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
