融合人工蜂群和混沌映射的混合视频水印算法

A Hybrid Video Watermarking Algorithm Based on Artificial Bee Colony and Chaotic Mapping

为了解决运动矢量搜索效率低下、水印信息嵌入单一等问题,融合自适应人工蜂群和Powell局部搜索,提出一种基于独立分量分析的运动目标检测方法。首先采用自适应搜索参数动态调整邻域搜索范围,使人工蜂群算法快速收敛于全局最优,然后将人工蜂群输出的所有蜜源进行K均值聚类,克服K均值聚类结果对初始聚类中心的依赖,再将聚类划分结果进行Powell局部搜索,加快方法收敛的速度。采用独立分量设计运动目标最优化问题,并利用改进方法求解最优解,从而提取视频序列中的运动分量。利用Logistic-正弦映射进行混沌加密,对加密后的水印图像进行Arnold映射置乱,将最终水印信息嵌入B帧和P帧中,在提高视频数据抗攻击的同时,增强视频数据的真实完整性。仿真结果表明,该混合水印嵌入算法在鲁棒性和脆弱性方面有良好的表现。

In order to solve the problem that the motion vector search is inefficient and the embedded watermark information is single, this paper integrates adaptive artificial bee colony and Powell local search to realize a moving object detection method based on independent component analysis. Adaptive search parameters are used to adjust neighborhood search scope dynamically, that makes artificial bee colony algorithm quickly converge to global optimal and achieve a more optimal solution. Then, all nectaries will be clustered by K-mean to be dependence of clustering result on the initial center, and then clustering results are divided into Powell local search, which accelerate the algorithm convergence speed. The motion component optimization problem is designed by using the independent component, and the optimal solution is solved by the improved method to extract the motion component in the video sequence. By using Logistic-sinusoidal mapping for chaotic encryption, the Arnold map is scrambled on the encrypted watermark image, and the final watermark information is embedded in B frame and P frame, and the real integrity of the video data is enhanced while improving the video data attack. The simulation results show that the hybrid watermark embedding algorithm has good performance in robustness and vulnerability.