Deep learning-based biometric image feature extraction for securing medical images through data hiding and joint encryption–compression

Abstract

Images are promising information carriers when compared to other media documents in the healthcare domain. However, digital data transmission over unprotected wired or wireless networks poses a threat to the security of healthcare systems. As a result, the issue of copyright violation and identity theft can occur due to the unauthorised use of these data. This paper proposes a new secure method under a framework that embeds biometric fingerprint image features in a medical image without any perceptual distortion. This paper uses ResNet152 for biometric image feature extraction in the first stage and features to generate a secret key for embedding in the second stage. The method combines encryption and compression scheme based on a generated key, novel chaotic map and Huffman coding to enhance the security of medical images while reducing the storage consumption or bandwidth requirements if images are transmitted to remote servers. Experimental results show that the proposed method presents superior security with high imperceptibility and compression performance, ensuring its effectiveness as an image protection mechanism for medical applications. Extensive experimental results show that the proposed method achieves an average peak signal-to-noise ratio (PSNR) that is above 54 dB, a structural similarity index measure (SSIM) close to 1, a bit error rate (BER) of 0 and a normalised correlation (NC) of 1. Moreover, this method compresses the images up to 70% when tested on three standard datasets.