Visual Cryptography Using Q'tron Neural Networks

• Main Authors: Su-Chen Chiang, 江素貞
• Other Authors: Tai-Wen Yue
• Format: Others
• Language: en_US
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/24467923987133795674

博士 === 大同大學 === 資訊工程學系(所) === 92 === Visual cryptography is a cryptographic scheme to achieve secret sharing. For example, it decomposes a secret image into n shares which are distributed to the participants, such that only qualified subsets of participants can "visually" recover the secret image. The "visual" recovery consists of xeroxing the shares onto transparencies, and then stacking them. The secret image will reveal without any cryptographic computation. Originally, the cryptographic paradigm introduced by Naor and Shamir has some drawbacks. This dissertation proposes a novel technique using neural networks (NNs) to fulfill visual cryptography schemes with some extended capabilities: i) the access schemes are described using a set of graytone images, and ii) the codebooks to fulfill them are not required; and iii) the size of share images is the same as the size of target images. The neural network model to conduct this research is called quantum neural-network (Q'tron NN; for short) model. It is an energy-driven NN model. A Q'tron NN is able to achieve local-minima free if it is constructed as a known-energy system and noise-injected, to be detailed in the dissertation. To fulfill an access scheme of visual cryptography, two energy sub-terms, which describe the image-halftoning rule and share-stacking rule, are considered to build the Q'tron NN. The proposed Q'tron NN structures are quite general and, hence, can be applied to fulfill any access schemes of visual cryptography. Some applications of visual cryptography based on the Q'tron NN approach are also discussed, including message concealment, visual authorization, and semipublic encryption.