A Semi-blind Watermarking Method for Authentication of Face Images Using Autoencoders

Recent advents of the internet have made accessibility of people to digital data such as audio, images, and videos much easier. Meanwhile, one of the cases that adversaries take advantage of is the people's face images that are available across the web. Digital watermarking is used to authenticate the original owner of the images and protect their copyright. With the help of digital watermarking, hidden data is embedded inside the image. Recently, neural networks such as autoencoders are one of the most popular models that are used in many fields. Neural networks are capable of understanding all kinds of raw data such as images and videos. In this paper, we present a method for embedding the user's national ID in their face images using autoencoders. The proposed autoencoder is trained with a dataset contains face images. The image is coded into some code using the autoencoders' encoder. Then, the national ID is embedded in this code and the modified code is reconstructed using the decoder to form the watermarked image. To extract the watermark, the watermarked image is encoded with the encoder and the watermark is extracted. Experiment results show that our model recovers the watermark with high accuracy and it is resistant against JPEG attacks. Moreover, the quality of the watermarked images is acceptable, and their SSIM compare to the original image is about 90%.