The ISC International Journal of Information Security

Abstract This study presents a resilient and efficient architecture for securely distributing secrets to the public across untrusted networks. The proposed method integrates Shamir’s Verifiable Secret Sharing with AES-GCM encryption to provide strong confidentiality and authentication guarantees. Each share is reinforced with cryptographic hash-based signatures and imperceptibly embedded within cover images using a neural steganographic framework based on an Attention U-Net enhanced with transformer mechanisms and Squeeze-and-Excitation blocks, allowing the system to place data in visually insensitive regions adaptively. The training process leverages a joint perceptual and structural loss function, ensuring high visual fidelity while preserving critical image features for robust message recovery. Experimental evaluations demonstrate superior performance in Peak Signal-to-Noise Ratio and Structural Similarity Index Measure, and a minimal Bit Error Rate across various distortions, including noise, blurring, and JPEG compression. Compared to existing methods, the framework provides enhanced protection against fraudulent participants or dealers, eliminates reliance on secure private channels, and enables the reuse of system components, offering a comprehensive solution for safe, verifiable secret sharing.