Steganography is an approach that fabricates a confidential data within a cover media to obtain an stego-media without causing any noticeable differences. In comparison to text, audio, and video, image steganography becomes more prevalent due to social media, high invisibility, and enormous payload. This paper introduces an image steganographic technique that uses Difference of Gaussian (DoG) edge detection and its dilated version to classify edge pixels into three classes: non-edge, dilated-DoG edge, and DoG edge. In order to hide the secret bits within the cover pixels with X:Y:Z ratio, where, for any X, Y, and Z, X < Y < Z, the encoder employs all three classes. Experimental results indicate an average payload of 2.45 bpp with an average PSNR of 39.08 dB and an average SSIM of 0.986, demonstrating the method’s effectiveness over existing approaches. Additionally, SR-Net steganalysis confirms the method’s security and ability to remain undetectable.