Enhanced Image Steganography Using Secure Random Pixel Distribution

ABSTRACT Image steganography often faces the issue of noticeable embedding patterns, which render conventional LSB‐based techniques vulnerable to both statistical and machine‐learning steganalysis. The present research offers a solution to this problem by presenting a Secure Random Pixel Distribution (SRPD) framework that integrates AES‐based cryptographic randomization along with a lightweight module LSB approach to make pixel changes unpredictable and with minimal distortion at the same time. The technique offers the option of encrypted coordinates, which can be used for either embedding or external deployment, providing flexibility for various deployment situations. Experimental testing on ten benchmark images reveals the excellent performance of SRPD, with PSNR values ranging from 70 to 85 dB, SSIM scores exceeding 0.998, and MSE values as low as 0.0002. The security check through chi‐square and RS analysis provides p ‐values greater than 0.85 and RS deviations less than 0.03%, demonstrating that SRPD is still “NOT DETECTED” by conventional steganalytic methods. Furthermore, the method's linear computational complexity is accompanied by a very low overhead from AES encryption. In summary, SRPD is a steganographic system that is secure, imperceptible, and cost‐effective in terms of computational resources; thus, it can be used for secret communication and privacy‐preserving multimedia applications.