当前位置: X-MOL 学术IEEE Trans. Multimedia › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Perceptual Image Hashing With Texture and Invariant Vector Distance for Copy Detection
IEEE Transactions on Multimedia ( IF 7.3 ) Pub Date : 2020-06-03 , DOI: 10.1109/tmm.2020.2999188
Ziqing Huang , Shiguang Liu

Content-based image copy detection has become one of the important technologies in copyright protection, where two major processes, content-based feature extraction and matching are included. However, it is certainly true that enough storage space is required to establish feature database for matching, which greatly increases time and storage consumption, as well as lacks flexibility. Fortunately, perceptual image hashing is a good strategy to address these problems, in which content-based features are extracted and further encoded to hash codes. On the one hand, content-based features provide and ensure higher copy detection accuracy, while on the other hand, hash codes instead of feature database reduce storage space and improve time efficiency. Meanwhile, a better balance between robustness and discrimination is one of the most objectives of image hashing, which is conducive to its application in multimedia management and security. Consequently, we present an effective image hashing method for copy detection. Specifically, to obtain perceptual robustness against to copy attacks, we extract the global statistical characteristics in gray-level co-occurrence matrix (GLCM) to reveal texture changes. Then, to make up the discrimination limitation, we leverage the local dominant DCT coefficients from the first row/column in each sub-image to calculate vector distance. Finally, two kinds of complementary information (global feature via texture and local feature via vector distance) are simultaneously preserved to generate hash codes. Various experiments performed on benchmark database indicate that our proposed perceptual image hashing provides higher detection accuracy and better balance between robustness and discrimination than the state-of-the-art algorithms.

中文翻译:

用于复制检测的具有纹理和不变矢量距离的感知图像哈希

基于内容的图像复制检测已成为版权保护的重要技术之一,其中包括基于内容的特征提取和匹配两个主要过程。但是,建立特征库进行匹配确实需要足够的存储空间,这大大增加了时间和存储消​​耗,并且缺乏灵活性。幸运的是,感知图像哈希是解决这些问题的好策略,其中基于内容的特征被提取并进一步编码为哈希码。一方面,基于内容的特征提供并保证了更高的复制检测精度,另一方面,哈希码代替特征数据库减少了存储空间,提高了时间效率。同时,更好地平衡鲁棒性和判别性是图像散列的最大目标之一,有利于其在多媒体管理和安全方面的应用。因此,我们提出了一种用于复制检测的有效图像散列方法。具体来说,为了获得对复制攻击的感知鲁棒性,我们提取灰度共生矩阵(GLCM)中的全局统计特征来揭示纹理变化。然后,为了弥补区分限制,我们利用每个子图像中第一行/列的局部主导 DCT 系数来计算向量距离。最后,同时保留两种互补信息(通过纹理的全局特征和通过向量距离的局部特征)以生成哈希码。
更新日期:2020-06-03
down
wechat
bug