Double image encryption algorithm based on neural network and chaos
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TL;DR: Wang et al. as discussed by the authors proposed a double image encryption algorithm based on convolutional neural network (CNN) and dynamic adaptive diffusion, which not only ensures the security of double image but also improves the encryption efficiency and reduces the possibility of being attacked.
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Abstract: To realize the secure transmission of double images, this paper proposes a double image encryption algorithm based on convolutional neural network (CNN) and dynamic adaptive diffusion. This scheme is different from the existing double image encryption technology. According to the characteristics of digital image, we design a dual-channel (digital channel / optical channel) encryption method, which not only ensures the security of double image, but also improves the encryption efficiency and reduces the possibility of being attacked. First, a chaotic map is used to control the initial values of the 5D conservative chaotic system to enhance the security of the key. Secondary, in order to effectively resist known-plaintext attack and chosen-plaintext attack, we employ a chaotic sequence as convolution kernel of convolution neural network to generate plaintext related chaotic pointer to control the scrambling operation of two images. On this basis, a novel image fusion method is designed, which splits and fuses two images into two different parts according to the amount of information contained. In addition, a dual-channel image encryption scheme, optical encryption channel and digital encryption channel, is designed for the two parts after fusion. The former has better parallelism and higher encryption efficiency, while the latter has higher computational complexity and better encryption reliability. Especially in the digital encryption channel, a new dynamic adaptive diffusion method is designed, which is more flexible and secure than the existing encryption algorithm. Finally, numerical simulation and experimental analysis verify the feasibility and effectiveness of the scheme.
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Citations
A new one-dimensional chaotic map for image encryption scheme based on random DNA coding
Qin Liang,Congxu Zhu +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel one-dimensional sine - cosine chaotic map (SCCM) to address the shortcomings of existing onedimensional chaotic maps.
92
Chaos-Based Image Encryption: Review, Application, and Challenges
TL;DR: Chaos has been one of the most effective cryptographic sources since it was first used in image-encryption algorithms as mentioned in this paper , and the unique attributes of chaos, such as sensitivity to initial conditions, topological transitivity, and pseudo-randomness, are conducive to cross-referencing with other disciplines and improving imageencryption methods.
88
A stable meaningful image encryption scheme using the newly-designed 2D discrete fractional-order chaotic map and Bayesian compressive sensing
TL;DR: Wang et al. as discussed by the authors proposed a stable image encryption scheme to create visually secure cipher image by using new fractional-order chaotic map, Bayesian compressive sensing and DVT embedding.
88
Cryptanalysis of an image encryption algorithm using quantum chaotic map and DNA coding
Heping Wen,Yiting Lin +1 more
TL;DR: A cryptanalysis of the Quantum Chaotic Map and DNA Coding image encryption algorithm reveals inherent security problems, including an equivalent key and lack of confusion and diffusion, allowing for a proposed attack method to crack the algorithm with low complexity.
83
Visually asymmetric image encryption algorithm based on SHA-3 and compressive sensing by embedding encrypted image
TL;DR: Wang et al. as mentioned in this paper presented a new asymmetric image encryption and hiding algorithm based on SHA-3 and compressive sensing, which is able to resist the chosen-plaintext attack (CPA) and the known-plain text attack (KPA), and the experimental results show that the algorithm has strong imperceptibility and key sensitivity.
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References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 01 Jan 2015
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
51.9K
Fully convolutional networks for semantic segmentation
Jonathan Long,Evan Shelhamer,Trevor Darrell +2 more
- 07 Jun 2015
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Fully Convolutional Networks for Semantic Segmentation
TL;DR: Fully convolutional networks (FCN) as mentioned in this paper were proposed to combine semantic information from a deep, coarse layer with appearance information from shallow, fine layer to produce accurate and detailed segmentations.
10.6K