Edge-Adaptive High-Capacity Image Steganography Using Hybrid Edge Detection and MSB Embedding

In this paper, a novel hybrid edge-detection steganography technique is proposed, which greatly increases the payload capacity without losing much of its invisibility. Conventional least significant bit (LSB) steganography has a low payload capacity and is sensitive to statistical analysis. Our approach combines Canny and Sobel edge-detection methods to find the optimal embedding regions and then performs Most Significant Bit (MSB) modifications in edge areas where the human visual system (HVS) is less sensitive to changes. Experimental results show that the performance of our proposed method outperforms conventional LSB-based steganographic methods by an average of 42.3% in payload capacity, while maintaining a PSNR greater than 38 dB and an SSIM above 0.95. The proposed method is also more robust against statistical attacks, such as chi-square analysis and RS steganalysis, which are critical challenges in secure data transmission.