StegoFace - Deep Learning-Based ID Image Security

Abstract: Identity verification processes predominantly depend on photo ID cards, which are at risk of fraudulent modifications and photo substitution methods. Conventional security techniques such as watermarks, microtext, and biometric verification exhibit limitations, rendering ID images vulnerable to tampering. To tackle this challenge, we introduce StegoFace, a steganographic model based on deep learning that improves the security of ID images by embedding concealed authentication data within facial images. The system utilizes Deep Convolutional Neural Networks (CNNs), Binary Error-Correcting Codes (BECC), and an autoencoder-decoder framework to ensure the secure embedding and retrieval of data while maintaining the integrity of the image. The Recurrent Proposal Network (RPN) effectively identifies facial areas for accurate message embedding, improving tamper detection and resilience against noise and compression. Experimental findings indicate that StegoFace successfully hides and retrieves concealed messages with minimal visual distortion, offering a strong, scalable, and cost-effective approach for secure identity verification in government-issued IDs, travel documents, and access control systems.