Robust image hashing with multidimensional scaling

Robust image hashing via DCT and LLE

Image hashing with color vector angle

Robust image hashing with embedding vector variance of LLE

\n Image hashing is an efficient technique of many multimedia systems, such as image retrieval, image authentication and image copy detection. Classification between robustness and discrimination is one of the most important performances of image hashing. In this paper, we propose a robust image hashing with singular values of quaternion singular value decomposition (QSVD). The key contribution is the innovative use of QSVD, which can extract stable and discriminative image features from CIE L*a*b* color space. In addition, image features of a block are viewed as a point in the Cartesian coordinates and compressed by calculating the Euclidean distance between its point and a reference point. As the Euclidean distance requires smaller storage than the original block features, this technique helps to make a discriminative and compact hash. Experiments with three open image databases are conducted to validate efficiency of our image hashing. The results demonstrate that our image hashing can resist many digital operations and reaches a good discrimination. Receiver operating characteristic curve comparisons illustrate that our image hashing outperforms some state-of-the-art algorithms in classification performance.

Robust Image Hashing With Singular Values Of Quaternion SVD

A robust image hashing using locally linear embedding (LLE) is presented. The image is first rescaled to a fixed size. Then the image is divided into key-dependent overlapping blocks. Every blocks are reshaped to vectors and combined to the matrix. The LLE algorithm is applied to the matrix. The reconstruction weights coefficients reflect the intrinsic geometric properties of image and are invariant basicly to ordinary image processing and several geometric transformation. The quantization scheme is devised to compress the coefficients matrix and form the binary image hash. Test results indicate that the proposed method is robust against content-preserving modifications such as JPEG compression, filtering and affine transformation. Hashes of different images have very low collision probability.

Zhizhong Ding

Papers

Robust image hashing using locally linear embedding