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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 3675459, 12 pages
Research Article

Batch Image Encryption Using Generated Deep Features Based on Stacked Autoencoder Network

1School of Computer and Information Science, Southwest University, Chongqing, China
2Network Centre, Chongqing University of Education, Chongqing, China

Correspondence should be addressed to Fei Hu; moc.361@1zte

Received 8 November 2016; Accepted 30 January 2017; Published 28 February 2017

Academic Editor: Maria L. Gandarias

Copyright © 2017 Fei Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Chaos-based algorithms have been widely adopted to encrypt images. But previous chaos-based encryption schemes are not secure enough for batch image encryption, for images are usually encrypted using a single sequence. Once an encrypted image is cracked, all the others will be vulnerable. In this paper, we proposed a batch image encryption scheme into which a stacked autoencoder (SAE) network was introduced to generate two chaotic matrices; then one set is used to produce a total shuffling matrix to shuffle the pixel positions on each plain image, and another produces a series of independent sequences of which each is used to confuse the relationship between the permutated image and the encrypted image. The scheme is efficient because of the advantages of parallel computing of SAE, which leads to a significant reduction in the run-time complexity; in addition, the hybrid application of shuffling and confusing enhances the encryption effect. To evaluate the efficiency of our scheme, we compared it with the prevalent “logistic map,” and outperformance was achieved in running time estimation. The experimental results and analysis show that our scheme has good encryption effect and is able to resist brute-force attack, statistical attack, and differential attack.