ABSTRACT

            Secret image sharing is a technique for protecting image by splitting secret image into many

shadow images that are transmitted and stored separately. In the scheme proposed in this paper each

image is get assembled in an disorder at each entry of user. Exploit local information alone, and

further evaluate if existing implementations of this computational model suffice to match human

performance. Surprisingly, in our series of human studies and machine experiments, we find that a

simple bag-of-words based majority-vote-like strategy is an accurate functional model of how

humans recognize jumbled images.