An efficient chaos-based image encryption algorithm using real-time object detection for smart city applications

混沌(操作系统) 加密 计算机科学 计算机视觉 人工智能 图像(数学) 对象(语法) 基于对象 计算机安全
作者
Kedar Nath Singh,Om Prakash Singh,Naman Baranwal,Amit Kumar Singh
出处
期刊:Sustainable Energy Technologies and Assessments [Elsevier]
卷期号:53: 102566-102566 被引量:19
标识
DOI:10.1016/j.seta.2022.102566
摘要

Currently, an increasing number of digital images are being generated by and for the general public and professionals. Simultaneously, the protection of these images is crucial for various emerging applications. Encryption is a popular technique for protecting image data confidentiality and privacy. There are multiple existing studies on image encryption, however, most of them are computationally overhead and do not achieve high security. Considering that time cost and security have become the most important factors, designing an efficient encryption algorithm for digital images are necessary. In this paper, a secure chaos-based image encryption algorithm for smart city applications is proposed. First, the You Only Look Once v3 object detection algorithm is used to quickly identify the objects of an image. Second, initialisation and generation of a key is performed with confusion and diffusion operations to get an encrypted object image. This algorithm for encryption has the advantage of strong security performance combined with speed, which is not available in the general encryption system. Experimental results show that this proposed algorithm outperforms other state-of-the-art schemes. • A secure chaos-based image encryption algorithm for smart city applications is proposed. • A neural network based object detection method is used to identify the significant object(s) of an image before encryption. • Proposed algorithm is secure and efficient than similar techniques.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
LMX完成签到 ,获得积分10
1秒前
粟裕的风完成签到,获得积分10
1秒前
shuaishuyi完成签到,获得积分10
1秒前
2秒前
桐桐应助swag采纳,获得10
3秒前
duduguai完成签到,获得积分10
3秒前
wang发布了新的文献求助10
3秒前
冷酷严青发布了新的文献求助10
3秒前
vilheim完成签到,获得积分10
3秒前
3秒前
小橙子完成签到,获得积分10
4秒前
Atlantis完成签到,获得积分10
4秒前
4秒前
天真听筠完成签到 ,获得积分10
5秒前
山神发布了新的文献求助10
5秒前
生动高丽完成签到,获得积分10
5秒前
lee1984612完成签到,获得积分10
5秒前
tcf完成签到,获得积分10
6秒前
6秒前
超表面发布了新的文献求助10
6秒前
111关注了科研通微信公众号
7秒前
认真的不评完成签到,获得积分10
7秒前
健行美好发布了新的文献求助10
7秒前
DDD完成签到,获得积分10
8秒前
8秒前
8秒前
lq完成签到 ,获得积分10
8秒前
烟花应助wang采纳,获得10
9秒前
qingzhiwu完成签到,获得积分10
9秒前
Grayson完成签到,获得积分10
9秒前
10秒前
10秒前
Hot完成签到,获得积分10
10秒前
周以筠完成签到 ,获得积分10
10秒前
方源完成签到 ,获得积分20
10秒前
神勇冰岚发布了新的文献求助10
10秒前
10秒前
11秒前
11秒前
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Iron toxicity and hematopoietic cell transplantation: do we understand why iron affects transplant outcome? 2000
List of 1,091 Public Pension Profiles by Region 1021
Teacher Wellbeing: Noticing, Nurturing, Sustaining, and Flourishing in Schools 800
Efficacy of sirolimus in Klippel-Trenaunay syndrome 500
上海破产法庭破产实务案例精选(2019-2024) 500
EEG in Childhood Epilepsy: Initial Presentation & Long-Term Follow-Up 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5477103
求助须知:如何正确求助?哪些是违规求助? 4578993
关于积分的说明 14366029
捐赠科研通 4507069
什么是DOI,文献DOI怎么找? 2469632
邀请新用户注册赠送积分活动 1456830
关于科研通互助平台的介绍 1430868