COUNTERFIET DETECTION IN NATIONAL IDENTITY CARDS USING IMAGE STEGANOGRAPHY

Abstract:  A national identity document is an identity card with a photo, usable as an identity card at least inside  the country, and which is issued by an official authority. The most common applications for these smart  cards are smart to travel documents, electronic IDs, electronic signatures, municipal cards, key cards used  to access secure areas or business infrastructures, social security cards, etc. These documents have several  security features which mitigate and combat document forgery. As these security systems are difficult to  circumvent, criminal attacks on ID verification systems are now focusing on fraudulently obtaining genuine  documents and the manipulation of the facial portraits. Trusted identity is a vital component of a well functioning society. To reduce risks related to this fraud problem, it is necessary those governments and  manufacturer of IDs continuously develop and improve security measures. With this in mind, we introduce  the first efficient steganography method – StegoCard – which is optimized for facial images printed in  common IDs. StegoCard is an end-to-end facial image steganography model that is formed by n Deep  Convolutional Auto Encoder, that can conceal a secret message in a face portrait and, hence, producing the  stego facial image, and a Deep Convolutional Auto Decoder, which is able to read a message from the stego  facial image, even if it is previously printed and then captured by a digital camera. Facial images encoded  with our StegoCard approach outperform the StegaStamp generated images in terms of their perception  quality. Peak Signal-to-Noise Ratio, hiding capacity and imperceptibility results on the test set are used to  measure the performance.