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Sharing Numerous Images Secretly with Reduced Possessing Load

Chen C-C., Chien Y-W.

Fundamenta Informaticae

2008

Vol. 86, nr 4

447-458

This work presents a secret image sharing approach for sharing several secret images while reducing each participant's possessing load to only one shared image. The proposed approach employs a random image and a hash function to create corresponding random-like shift images for each secret image, and then publishes the generated images. The random image is then secretly shared to each participant, such that each participant can only possess one shared image to recover the random image, thus enabling the computation of all secret images. The contiguous hash results modify the secret images to random-like public images that are secure for malicious attacks. Experimental results indicate that the proposed method can share several secret images but reduce the possessing load of each participant efficiently. Attacks on shared or public images are also discussed.

Full-Searching-Equivalent Vector Quantization Method Using Two-Bounds Triangle Inequality

Chang C-C., Chen C-C.

Fundamenta Informaticae

2007

Vol. 76, nr 1-2

25-37

The encoding process of vector quantization (VQ) is indeed computational complex and time consuming. Compared with actual Euclidean distance computation, some inequalities can generate estimations with less computation to filter out the impossible codevectors as well as to reduce the computation time. In this paper, we introduce a new estimation for the Euclidean distance using two-bounds triangle inequality. The experimental results show that our proposed scheme can reduce Euclidean distance computation by 71% to 94% for full search. Having been proved, our proposed scheme can reduce the computing time by 42% to 51%.