Exploiting Human Visual Limitations for High Capacity Information Hiding in Digital Images using Alpha Channel

Information hiding in digital images is a critical technique for secure communication in the era of high-volume multimedia exchange. While many existing methods embed secret data by modifying image parameters in the spatial or frequency domains, they often overlook local pixel characteristics and the potential of the alpha channel for data embedding. This paper proposes a flexible Least Significant Bit (LSB) information hiding framework that adaptively adjusts the number of embedded bits based on pixel intensity ranges. By configuring the embedding function, the framework can support multiple strategies: (i) allocating more bits to low-intensity regions and fewer to high-intensity ones, (ii) leveraging Weber’s Just Noticeable Difference (JND) principle to embed more data in high-intensity pixels, and (iii) combining both approaches to embed more in low- and high-intensity regions while reducing payload in mid-intensity regions. Additionally, the alpha channel is exploited to further enhance embedding capacity while preserving visual fidelity. Experimental results on standard test images demonstrate that the proposed adaptive framework significantly outperforms existing methods in embedding capacity, while maintaining high visual quality with PSNR values consistently above practical thresholds.