Contrast Optimization for Size Invariant Visual Cryptography Scheme.

Area of Science:

• Computer Science
• Information Security
• Image Processing

Background:

• Traditional visual cryptography schemes (VCS) suffer from pixel expansion.
• Size-invariant VCS (SI-VCS) addresses pixel expansion but requires contrast optimization.
• High contrast in recovered images is crucial for practical SI-VCS applications.

Purpose of the Study:

• To investigate and optimize image contrast in size-invariant visual cryptography schemes (SI-VCS).
• To develop methods for maximizing contrast by stacking 't' out of 'n' shadows in a (k, n)-SI-VCS.
• To provide multiple optimal contrast solutions for SI-VCS.

Main Methods:

• Formulating contrast maximization as an objective function within a (k, n)-SI-VCS framework.
• Employing linear programming to find ideal contrast values for 't' shadows.
• Utilizing optimization-based design, including the ideal point and lexicographic methods, for multi-contrast maximization.
• Developing a technique for multiple maximum contrasts when using Boolean XOR for secret recovery.

Main Results:

• An approach to optimize contrast in SI-VCS by stacking 't' shadows is successfully developed.
• Linear programming enables the calculation of ideal contrast for a given number of shadows.
• Optimization techniques provide multiple distinct optimal contrast levels for (k, n)-SI-VCS.
• Experimental validation confirms significant improvements in recovered image contrast.

Conclusions:

• The proposed methods effectively optimize image contrast in SI-VCS.
• The techniques offer flexibility by providing multiple contrast options.
• This research advances the practical applicability of SI-VCS for secure image sharing.