Evidence map›Paper›PMID 34441140›Full record

ArticleEntropy (Basel, Switzerland)2021

A Security-Enhanced Image Communication Scheme Using Cellular Neural Network.

Heping Wen, Jiajun Xu, Yunlong Liao, Ruiting Chen, Danze Shen, Lifei Wen, Yulin Shi, Qin Lin, Zhonghao Liang, Sihang Zhang and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Entropy in Image Analysis III.Entropy (Basel, Switzerland) · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Heping WenZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Jiajun XuZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Yunlong LiaoZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Ruiting ChenZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Danze ShenZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Lifei WenZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Yulin ShiZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Qin LinZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Zhonghao LiangZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Sihang ZhangZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Yuxuan LiuZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Ailin HuoZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Tong LiZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Chang CaiZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Jiaqian WenZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.
Chongfu ZhangZhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.

Funding

National Natural Science Foundation of China 62071088
6 · The paper itself

Abstract

In the current network and big data environment, the secure transmission of digital images is facing huge challenges. The use of some methodologies in artificial intelligence to enhance its security is extremely cutting-edge and also a development trend. To this end, this paper proposes a security-enhanced image communication scheme based on cellular neural network (CNN) under cryptanalysis. First, the complex characteristics of CNN are used to create pseudorandom sequences for image encryption. Then, a plain image is sequentially confused, permuted and diffused to get the cipher image by these CNN-based sequences. Based on cryptanalysis theory, a security-enhanced algorithm structure and relevant steps are detailed. Theoretical analysis and experimental results both demonstrate its safety performance. Moreover, the structure of image cipher can effectively resist various common attacks in cryptography. Therefore, the image communication scheme based on CNN proposed in this paper is a competitive security technology method.

Indexed as

cellular neural networkchaosimage encryptionsecure communication

Identifiers

PMID34441140
PMCPMC8392563

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.