TY - JOUR
T1 - Steganography for medical record image
AU - Hua, Chunjun
AU - Wu, Yue
AU - Shi, Yiqiao
AU - Hu, Menghan
AU - Xie, Rong
AU - Zhai, Guangtao
AU - Zhang, Xiao Ping
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/10
Y1 - 2023/10
N2 - Medical record images in EHR system are users’ privacy and an asset, and there is an urgent need to protect this data. Image steganography can offer a potential solution. A steganographic model for medical record images is therefore developed based on StegaStamp. In contrast to natural images, medical record images are document images, which can be very vulnerable to image cropping attacks. Therefore, we use text region segmentation and watermark region localization to combat the image cropping attack. The distortion network has been designed to take into account the distortion that can occur during the transmission of medical record images, making the model robust against communication induced distortions. In addition, based on StegaStamp, we innovatively introduced FISM as part of the loss function to reduce the ripple texture in the steganographic image. The experimental results show that the designed distortion network and the FISM loss function term can be well suited for the steganographic task of medical record images from the perspective of decoding accuracy and image quality.
AB - Medical record images in EHR system are users’ privacy and an asset, and there is an urgent need to protect this data. Image steganography can offer a potential solution. A steganographic model for medical record images is therefore developed based on StegaStamp. In contrast to natural images, medical record images are document images, which can be very vulnerable to image cropping attacks. Therefore, we use text region segmentation and watermark region localization to combat the image cropping attack. The distortion network has been designed to take into account the distortion that can occur during the transmission of medical record images, making the model robust against communication induced distortions. In addition, based on StegaStamp, we innovatively introduced FISM as part of the loss function to reduce the ripple texture in the steganographic image. The experimental results show that the designed distortion network and the FISM loss function term can be well suited for the steganographic task of medical record images from the perspective of decoding accuracy and image quality.
KW - Digital image watermarking
KW - Electronic health records system
KW - Image steganography
KW - Medical record image
UR - https://www.scopus.com/pages/publications/85168310861
U2 - 10.1016/j.compbiomed.2023.107344
DO - 10.1016/j.compbiomed.2023.107344
M3 - 文章
C2 - 37603961
AN - SCOPUS:85168310861
SN - 0010-4825
VL - 165
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
M1 - 107344
ER -