https://doi.org/10.1140/epjs/s11734-025-01611-1
Regular Article
Neural network-driven parallel accelerated selective image encryption with semantic understanding
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
University of California, Irvine, Irvine, CA, USA
4
School of Software, Dalian University of Technology, 116621, Dalian, China
Received:
26
January
2025
Accepted:
31
March
2025
Published online:
23
April
2025
Existing batch image encryption schemes often directly encrypt images without considering their source. However, in real-life scenarios, these images are typically part of a larger collection stored in a directory on a computer or in the cloud. To efficiently protect those sensitive images distributed within the collection, we rethink selective image encryption (SIE) from the perspective of batch image encryption and proposes a novel SIE scheme using deep neural networks (DNN) and parallel computing. First, a DNN method is employed to identify sensitive images by analyzing their semantics. Next, a parallel cross-bitplane permutation algorithm is introduced to shuffle the retrieved images in bit-level. Then, a bi-directional diffusion strategy is employed to spread the encryption influence of a pixel to all images. Experimental results demonstrate the proposed scheme effectively understands the semantic of images, identifies sensitive images, and encrypts them with high permutation and diffusion efficiency. Further security analyses show that the proposed algorithm successfully resists various attacks with desired performance, including achieving high information entropy and low correlation coefficients in statistical analysis, and ideal values of the number of pixels changing rate (NPCR) and the unified averaged changing intensity (UACI) to resist differential attack, as well as high key sensitivity.
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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.
