A Blind Decryption Model for Thumbnail-Preserving Encryption: Image Reconstruction without a Key via an Improved Generative Adversarial Network

ZHU Liya; SUN Yana; REN Shuai; ZHANG Liukun; JIANG Donghua

doi:10.12263/DZXB.20250556

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A Blind Decryption Model for Thumbnail-Preserving Encryption: Image Reconstruction without a Key via an Improved Generative Adversarial Network

PAPERS | 更新时间：2026-06-16

- A Blind Decryption Model for Thumbnail-Preserving Encryption: Image Reconstruction without a Key via an Improved Generative Adversarial Network
- ACTA ELECTRONICA SINICA Vol. 54, Issue 3, Pages: 1094-1104(2026)
- 作者机构：
  
  1.长安大学电子与控制工程学院，陕西西安 710064
  2.长安大学信息工程学院，陕西西安 710064
  3.中山大学计算机学院，广东广州 510006
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62372062)
- DOI：10.12263/DZXB.20250556
  CLC： TP391;
- Received：26 June 2025，
  
  Accepted：06 March 2026，
  
  Published：25 March 2026
- 稿件说明：
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朱礼亚, 孙雅娜, 任帅, 等. 缩略图盲解密模型：利用改进生成对抗网络的免密钥图像重建[J]. 电子学报, 2026, 54(03): 1094-1104.

ZHU Liya, SUN Yana, REN Shuai, et al. A Blind Decryption Model for Thumbnail-Preserving Encryption: Image Reconstruction without a Key via an Improved Generative Adversarial Network[J]. Acta Electronica Sinica, 2026, 54(03): 1094-1104.
朱礼亚, 孙雅娜, 任帅, 等. 缩略图盲解密模型：利用改进生成对抗网络的免密钥图像重建[J]. 电子学报, 2026, 54(03): 1094-1104. DOI：10.12263/DZXB.20250556

ZHU Liya, SUN Yana, REN Shuai, et al. A Blind Decryption Model for Thumbnail-Preserving Encryption: Image Reconstruction without a Key via an Improved Generative Adversarial Network[J]. Acta Electronica Sinica, 2026, 54(03): 1094-1104. DOI：10.12263/DZXB.20250556

摘要

缩略图保持加密（Thumbnail-Preserving Encryption， TPE）是平衡云端图像隐私性和可用性的重要方法。为确保安全性，通常利用动态更新机制生成与原始图像信息相关联的密钥。针对图像信息非正常缺失或篡改，接收方无法获得正确的密钥进行解密的问题，设计了一种基于生成对抗网络的轻量化缩略图盲解密模型（Blind Decryption model based on Generative Adversarial Networks， BD-GAN）。通过对生成器、判别器和损失函数的改进和优化，提升解密图像的质量。生成器基于改进的U-Net网络，采用编码器-转码器-解码器的级联结构。在编码器和解码器中分别嵌入多尺度注意力融合模块（Multi-Scale Attention Fusion modules， MSAF），实现各尺度头部信息的跨层融合，在采样过程中最大限度保留图像细节，解决了多次采样和深层网络长距离依赖所导致的信息丢失问题。将U-Net网络的瓶颈层替换为由多个基础残差块级联堆叠构成的转码器，促进提取特征的高效传递与学习。为了解决局部判别器只输出最后一层局部特征，忽略中间层特征所含的多尺度语义与纹理信息的问题，改进并设计了具有多层特征反馈特性的局部判别器，返回多个中间层特征用于对抗训练。通过对不同尺度特征的逐层输出，提升判别器对多尺度纹理细节的感知能力。在损失函数优化方面，采用视觉感知损失、对抗损失和身份一致性损失的联合优化策略，将各损失项的线性组合作为优化目标，通过最小化目标函数进行训练，提升重建图像的视觉质量。实验结果表明，该模型能够对多种缩略图保持加密算法进行免密钥重建，解决了密钥有损情况下解密失败的问题。同时在不牺牲重建性能的前提下，具有更少的训练计算开销，有效地降低了部署成本。相比于采用U-Net网络和局部判别器的生成对抗网络模型，重建图像的峰值信噪比提升了0.632 8 dB，FID（Fréchet Inception Distance）性能提升了14.361 0。与去噪扩散概率解密模型相比，在保证重建效果的同时，参数量和计算量分别减少5.120 × 10

和2.807 × 10

以上。研究为通过身份验证和合法授权的用户提供了应急条件下的缩略图解密方案，提高了安全冗余度。

Abstract

Thumbnail-preserving encryption (TPE) is an important method to balance the privacy and availability of images in cloud environments. In pursuit of superior security

secret keys are generally generated using the image-related information under the assistance of a dynamic update mechanism. However

receivers may fail to obtain the correct keys due to the information loss or tampering. To address this issue

a lightweight blind decryption model based on generative adversarial networks (BD-GAN) is proposed for TPE images. This work devotes to better reconstruction performance of the image through the improvement of the generator and discriminator

and optimization of the loss function. In our scheme

the generator is designed based on an improved U-Net architecture

and it employs a cascaded structure composed of encoder

tra

nscoder

and decoder modules. Multi-scale attention fusion modules (MSAF) are embedded in the encoder and decoder

respectively

to achieve cross-layer fusion of multi-scale hierarchical information. By this means

image details are reserved to the maximum extent

and the problems of information loss caused by repeated sampling and long-range dependencies in deep networks are addressed. The bottleneck layer of the U-Net network is replaced with a transcoder composed of cascaded multiple residual blocks

so as to promote the efficient transmission and learning of extracted features. Moreover

to solve the problem that the local discriminator only utilizes the local features of the last layer and ignores the multi-scale semantic and texture information contained in the intermediate layer features

a local discriminator with multi-layer feature feedback is designed

which returns multiple intermediate layer features for adversarial training. Through the layer-by-layer output of features

the capability of perceiving the multi-scale textures is enhanced. In addition

a joint optimization strategy integrating visual perception loss

adversarial loss

and identity consistency loss is adopted to optimize the loss function. Specifically

the linear combination of these loss components serves as the optimization objective

and the model is trained by minimizing the overall loss to improve the visual quality of the reconstructed images. Experimental results show that the proposed model can perform for various TPE algorithms without any secret keys

and the failure of decryption can be avoided even if the secret keys are damaged. More importantly

on the premise of not degrading reconstruction performance

our model has lower training computational overhead and deployment cost. Compared with the traditional GAN employing a U-Net network and local discriminator

the peak signal to noise ratio (PSNR) values of the reconstructed images are improved by 0.632 8 dB

and the fréchet inception distance (FID) values are improved by

14.361 0. Notably

in comparison with the diffusion models

our model offers a satisfactory reconstructed image while reducing parameters and computational cost by more than 5.120 × 10

and 2.807 × 10

respectively. This study provides an effective emergency decryption scheme for users who have passed identity verification and legal authorization

which improves security redundancy.

关键词

Keywords

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