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A cheating prevention scheme for binary visual cryptography with homogeneous secret images

机译:具有均质秘密图像的二进制视觉密码的防欺诈方案

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摘要

In 1995, Naor and Shamir proposed the k-out-of-n visual cryptography scheme such that only more than or equal to k participants can visually recover the secret through superimposing their transparencies. Visual cryptography schemes have been extensively investigated since their invention and extended to numerous applications such as visual authentication and identification, steganography, and image encryption. In 2006, Horng et al. proposed that cheating is possible where some participants can deceive the remaining participants by delivering forged transparencies. Meanwhile, Horng et al. also proposed two cheating prevention schemes. One scheme, however, requires extra verification transparencies and the other needs larger transparencies. In other words, compared to visual cryptography, both schemes burden each participant with an additional problem of transparency management. In this paper, a more secure scheme is given to solve the cheating problem without extra burdens by adopting multiple distinct secret images. Moreover, for sharing these secret images simultaneously, the share construction method of visual cryptography is redesigned and extended by generic algorithms. Finally, the results of the experiment and security analysis show that not only the proposed scheme is more secure in comparison with the two previous cheating prevention schemes in the literature, but extra burdens are also eliminated. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:在1995年,Naor和Shamir提出了n出k的视觉密码方案,这样,只有多于或等于k个参与者才能通过叠加透明胶片来视觉恢复秘密。自从其发明以来,视觉密码方案已被广泛研究,并扩展到许多应用,例如视觉认证和识别,隐写术和图像加密。 2006年,Horng等人。有人建议作弊是可能的,因为有些参与者可以通过提供伪造的透明胶片来欺骗其余的参与者。同时,Horng等。还提出了两种防止作弊的方案。但是,一种方案需要额外的验证透明胶片,而另一种则需要更大的透明胶片。换句话说,与视觉加密相比,这两种方案都给每个参与者增加了透明度管理的额外问题。在本文中,通过采用多个不同的秘密图像,给出了一种更安全的方案来解决欺诈问题而没有额外的负担。而且,为了同时共享这些秘密图像,通过通用算法重新设计并扩展了视觉密码术的共享构造方法。最后,实验和安全性分析的结果表明,与文献中以前的两种作弊预防方案相比,该方案不仅更安全,而且消除了额外的负担。 (C)2007模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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