Recently, Internet becomes a most common medium for transferring critical data and the security of the transmitted data gains maximum priority. Image steganography has been developed as a well‐known model of data hiding which verifies the security level of the transferred data. The images offer high capacity, and the occurrence of accessibility over the Internet is more. An effective steganography model is required for achieving better embedding capacity and also maintaining the other variables in an acceptable value. This article introduces a new robust image steganography using Teaching Learning Based Optimization (TLBO) edge detection model. The TBLO is basically a metaheuristic algorithm which is inspired from the teaching and learning procedure in classrooms. The former stage indicates the learning from the teacher and the latter phase represents the interaction among the learners. The experimental validation takes place in a comprehensive way under several views and the outcome pointed out the superior results of the presented model.

中文翻译：

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基于教学学习的基于优化的智能城市边缘检测模型的鲁棒图像隐写术

近来，因特网成为用于传输关键数据的最普通的介质，并且所传输的数据的安全性获得了最大的优先权。图像隐写术已被开发为一种众所周知的数据隐藏模型，可验证传输数据的安全级别。图像提供高容量，并且通过Internet访问的可能性更多。需要有效的隐写术模型以实现更好的嵌入能力，并将其他变量保持在可接受的值。本文介绍一种使用基于教学学习的优化（TLBO）边缘检测模型的新型鲁棒图像隐写术。TBLO基本上是一种元启发式算法，其灵感来自教室的教学程序。前一个阶段表示向老师学习，后一个阶段表示学习者之间的互动。实验验证在几种观点下以全面的方式进行，结果指出了所提出模型的优越结果。