An Enhanced Color Visual Cryptography Technique Using Generated Adversarial Network (GANs)
Keywords:
— cryptography, encryption, security, privacy, networkAbstract
Hundreds of millions of people around the globe use different computing devices and services, like smartphones, laptops, and messaging apps. Visual cryptography (VC) is widely regarded as a highly secure way to encrypt images, making it essential for various important applications, such as maintaining the integrity of voting, protecting online transactions, and ensuring privacy. The core of VC involves turning secret images into several digital shares, which makes it impossible for anyone to reveal the original image from just one share. However, there are challenges in current VC implementations, such as issues with pixelation, high computational overhead, and diminished decryption fidelity, significantly impact its efficacy. To tackle these challenges, we enhance a new color visual cryptography technique using generated adversarial networks to generate secure encrypted shares. Notably, our scheme maintains non-expandability by preserving equal dimensions between the original secret image and its shares, thus reducing memory requirements while improving image fidelity. We test the proposed technique using a variety of standard benchmark images and apply established metrics to assess its resistance to cryptanalytic attacks, correlation strength, histogram characteristics, and overall encryption quality. Our findings show that the suggested technique provides improved image quality, more effective encryption, and almost ideal statistical features compared to current methods.
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