融合HVS计算模型的视频感知哈希算法研究

Video perceptual hashing fuse computational model of human visual system

感知哈希(perceptual hashing)是多媒体数据集到摘要集的单向映射,为多媒体数字内容的标识、检索、认证等应用提供了安全可靠的技术支撑。目前关于感知哈希算法的研究主要集中在不断提高其鲁棒性和安全性上,忽略了人的主要视觉感知特性,导致了算法的过鲁棒性问题。将人类视觉系统可计算模型融入视频感知哈希算法框架中,用模拟人眼感受野特征提取特性的Cortex变换进行通道分解,并使用时-空域对比度敏感函数、眼球移动函数、亮度适应性调整函数、子带内和子带间对比度掩蔽函数综合计算最小视觉差提取感知特征。在保证较好鲁棒性的前提下,算法中使用扩散分块的机制提高安全性,通过与已有算法之间的比较,结果表明,本文提出的算法在鲁棒性和安全性方面取得了有效折衷,同时也体现了主观感知与客观评测上的一致性。

Perceptual hashing is a function of mapping from multimedia digital presentations to a perceptual hash value, which provides a secure and reliable technical support in fields such as identification, retrieval, and certification of multimedia content. The current algorithms fail in taking sufficient human visual perceptual factors into consideration. With the improvement of their over-robustness, most of the algorithms can＇ t assure their securities. In this paper, a novel perceptual hashing algorithm is proposed. In order to simulate multi-channel features of the human visual system, a cortex transformation is combined with a computational model of the human visual system, which is designed by jointly considering four visual perceptual factors during the feature extraction stage, such as spatio-temporal contrast sensitivity function, eye movement, lightness adaptation, and intra-band and inter-band masking. Additionally, a diffusion mechanism is introduced into the preprocessing stage. The results suggest our proposed method could achieve better trade-offs between robust and secure resilient to various content-preserving manipulations, and also reflects the uniformity between subjective perception and objective evaluation