ENHANCED SECURITY MEASURES FOR NATIONAL IDENTITY CARD USING IMAGE STEGANOGRAPHY

Abstract

National identity documents are essential cards issued by official authorities, typically containing a photo and used for various purposes within a country, such as travel, electronic identification, and access to secure locations. These documents incorporate multiple security features to combat forgery, yet criminals increasingly target the manipulation of genuine documents and facial images. Trusted identity is crucial for societal function, necessitating continuous improvements in security measures by governments and ID manufacturers. To address this, we present StegoCard, a novel steganography method specifically designed for concealing messages within facial images commonly found on IDs. StegoCard employs a series of Deep Convolutional Auto Encoders to embed secret messages into facial portraits, creating stego facial images. These stego images can then be decoded by Deep Convolutional Auto Decoders, even after being printed and captured digitally. Our StegoCard approach outperforms existing methods like StegaStamp in terms of perceptual quality, as demonstrated by metrics such as Peak Signal-to-Noise Ratio, hiding capacity, and imperceptibility. By leveraging deep learning and steganography, StegoCard enhances the security of national identity documents, contributing to the preservation of trusted identities within society. KEYWORD:Deep Learning, Steganography,Recurrrent proposal Network(RPN)