• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Arabian Journal for Science and Engineering >Image Steganography Capacity Improvement Using Cohort Intelligence and Modified Multi-Random Start Local Search Methods
【24h】

Image Steganography Capacity Improvement Using Cohort Intelligence and Modified Multi-Random Start Local Search Methods

机译：使用队列智能和改进的多随机开始本地搜索方法提高图像隐写能力

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we have proposed two steganographic techniques which use JPEG compression on greyscale image to hide secret text. JPEG compression is based on discrete cosine transform technique. In order to improve the capacity, a quantization table of size $$16times 16$$ 16 × 16 is practiced instead of a standard JPEG quantization table. Also, the proposed work presents two novel optimization algorithms applied on steganography which are based on the concept of cohort intelligence (CI) with cognitive computing (CC) and Multi-random start local search (MRSLS) algorithm. CI is an emerging optimization algorithm inspired from social learning of one another. This algorithm is being tested to solve unconstrained, constrained and NP-hard combinatorial problems and shows promising results. CC involves self-learning systems and is an emerging area in the field of machine learning. In the proposed work, CI, CC and MRSLS which is inspired from duo-swapping approach and tested to solve NP-hard combinatorial problems, are combined and applied to steganography to produce good results. This work has modified the MRSLS algorithm and applied to steganography to test and validate our results with other comparable algorithms. Experiments are done to test six greyscale images. Experimental results will reveal the quality of stego-images and the secret message embedding capacity.
机译：在本文中，我们提出了两种隐写技术，它们在灰度图像上使用JPEG压缩来隐藏秘密文本。 JPEG压缩基于离散余弦变换技术。为了提高容量，实践了尺寸为$ 16×16×$ 16×16的量化表，而不是标准的JPEG量化表。此外，拟议的工作还提出了两种适用于隐写术的新型优化算法，它们基于具有认知计算（CC）的群组智能（CI）和多随机起始本地搜索（MRSLS）算法的概念。 CI是一种新兴的优化算法，灵感来自彼此的社交学习。该算法已经过测试，可以解决无约束，受约束和NP-hard组合问题，并显示出令人鼓舞的结果。 CC涉及自学习系统，并且是机器学习领域的新兴领域。在拟议的工作中，CI，CC和MRSLS受二重交换方法的启发并经过测试以解决NP-hard组合问题，并将其组合并应用于隐写术以产生良好的效果。这项工作已经修改了MRSLS算法，并应用于隐写术，以使用其他可比较的算法测试和验证我们的结果。进行了实验以测试六个灰度图像。实验结果将揭示隐秘图像的质量和秘密消息的嵌入能力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号