A Novel Technique Using Crayfish Optimization For Improved Pixel Selection And Block Scrambling Encryption Enhances Data Security For Cloud Computing.
This innovative approach offers effective solutions for protecting sensitive digital images stored and transmitted within cloud environments.
The digital age is defined by our reliance on cloud computing, enabling unparalleled access to network resources. Yet, with this convenience arises pressing concerns over security, particularly as sensitive data, such as images, face ever-evolving cyber threats. Traditional encryption techniques have struggled to effectively secure digital images, leading to potential vulnerabilities and unauthorized access.
To address these challenges, researchers have developed the Crayfish Optimization based Pixel Selection using Block Scrambling Based Encryption Approach (CFOPS-BSBEA). This novel technique not only enhances the protection of digital images but also optimizes the methods by which they are encrypted and embedded. The CFOPS-BSBEA method takes advantage of advanced algorithms to integrate steganography and encryption, presenting a three-stage protocol for image security.
The first step involves pixel selection, employing the Crayfish Optimization (CFO) algorithm to determine the most suitable pixels for embedding secret images. Inspired by the predation and competition behaviors of crayfish, the CFO algorithm iteratively searches for optimal pixel locations, ensuring minimal detection risk.
Following pixel selection, the technique applies Block Scrambling Based Encryption (BSBE), encoding the assembled secret images. This step introduces another layer of complexity to the encryption process, which is particularly effective against sophisticated attack strategies.
Finally, the CFOPS-BSBEA method executes the embedding and extraction processes, seamlessly embedding encoded images within cover images, thereby concealing sensitive data.
Experimental results highlight the CFOPS-BSBEA technique's superiority over existing models. Metrics such as Mean Squared Error (MSE), Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) demonstrate significant improvements, underscoring this approach's effectiveness.
"The CFOPS-BSBEA technique reaches effectual encryption results," the authors note. Initial observations revealed MSE values as low as 0.1680 and SSIM scores above 0.9990, indicating not only enhanced quality but also strong security postures.
The ramifications of the CFOPS-BSBEA technique extend far beyond this study, potentially paving the way for research addressing diverse forms of data encryption and security. Future enhancements could include adapting the model for various media types, improving real-time application responsiveness, and integrating advanced cryptographic methods to fortify defenses against unauthorized access.
Overall, the CFOPS-BSBEA method emerges as a significant advancement within the domain of cloud computing security, marrying clever optimization techniques to structural encryption methods. Its development signifies promising horizons for sensitive data protection, ensuring integrity and confidentiality remain intact.