Abstract
In contrast to encrypting the full secret image in classic image secret sharing (ISS), partial image secret sharing (PISS) only encrypts part of the secret image due to the situation that, in general, only part of the secret image is sensitive or secretive. However, the target part needs to be selected manually in traditional PISS, which is human-exhausted and not suitable for batch processing. In this paper, we introduce an adaptive PISS (APISS) scheme based on salience detection, linear congruence, and image inpainting. First, the salient part is automatically and adaptively detected as the secret target part. Then, the target part is encrypted into n meaningful shares by using linear congruence in the processing of inpainting the target part. The target part is decrypted progressively by only addition operation when more shares are collected. It is losslessly decrypted when all the n shares are collected. Experiments are performed to verify the efficiency of the introduced scheme.
Highlights
An image secret sharing (ISS) scheme encrypts a secret image into n shares and distributes them to n related participants
We introduce an adaptive partial image secret sharing (PISS) (APISS) scheme based on salience detection, Linear congruence (LC), and image inpainting
No information on the content of the secret image is decrypted when fewer than three shares are collected but with shape leakage
Summary
An image secret sharing (ISS) scheme encrypts a secret image into n shares and distributes them to n related participants. The secret image is decrypted when collecting any k or more shares. The widely studied principles of ISS techniques include visual secret sharing (VSS), known as, visual cryptography (VC) [4,5] and polynomial [6,7,8,9,10,11,12]. VSS for a (k, n)-threshold [11,13,14,15,16,17], usually outputs n shares printed onto transparent films, which are distributed to n participants. The advantage of VSS is that the secret can be recognized by the naked human eye when superposing any k or more shares. The traditional VSS approaches often have the disadvantages of large pixel expansion and poor image quality
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