FuzzyStego: An Adaptive Steganographic Scheme Using Fuzzy Logic for Optimizing Embeddable Areas in Spatial Domain Images

In the evolving landscape of secure communication, steganography has become increasingly vital to secure the transmission of secret data through an insecure public network. Several steganographic algorithms have been proposed using digital images with a common objective of balancing a trade-off between the payload size and the quality of the stego image. In the existing steganographic works, a remarkable distortion of the stego image persists when the payload size is increased, making several existing works impractical to the current world of vast data. This paper introduces FuzzyStego, a novel approach designed to enhance the stego image’s quality by minimizing the effect of the payload size on the stego image’s quality. In line with the limitations of traditional methods like Pixel Value Differencing (PVD), Transform Domain Techniques, and Least Significant Bit (LSB) insertion, such as image quality degradation, vulnerability to processing attacks, and restricted capacity, FuzzyStego utilizes fuzzy logic to categorize pixels into intensity levels: Low (L), Medium-Low (ML), Medium (M), Medium-High (MH), and High (H). This classification enables adaptive data embedding, minimizing detectability by adjusting the hidden bit count according to the intensity levels. Experimental results show that FuzzyStego achieves an average Peak Signal-to-Noise Ratio (PSNR) of 58.638 decibels (dB) and a Structural Similarity Index Measure (SSIM) of almost 1.00, demonstrating its promising capability to preserve image quality while embedding data effectively.