Inspite of progressive growth of cryptography, encrypting sensitive information of an image is still a computationally complex task. After reviewing existing literature, it is now known that security problems are yet not solved and there is an open scope of further research. In most recent times, it has been noticed that neural network has proven cost effective optimization mechanism in offering security towards images. However, such implementation are computationally expensive process and do not solve various diversified attacks on image. Hence, the prime purpose of proposed system is to introduce an analytical research methodology for presenting a sophisticated framework where deep neural network has been used for optimizing the performance of simple encryption approaches. The robustness of optimization principle is further added with chaotic map concept for enhanced security performance. The study outcome shows that proposed implementation offers much better security performance without any negative effect on image quality.

尽管加密技术逐渐发展，但是对图像的敏感信息进行加密仍然是一项计算复杂的任务。在回顾了现有文献之后，现在知道安全问题尚未解决，并且存在进一步研究的开放范围。在最近的时间里，已经注意到神经网络已经证明在提供图像安全性方面具有成本效益的优化机制。但是，这种实现是计算上昂贵的过程，并且不能解决对图像的各种多样化攻击。因此，提出的系统的主要目的是介绍一种分析研究方法，以提出一种复杂的框架，其中深度神经网络已用于优化简单加密方法的性能。优化原理的鲁棒性进一步添加了混沌映射概念，以增强安全性能。研究结果表明，所提出的实施方案提供了更好的安全性能，而对图像质量没有任何负面影响。