2 research outputs found
BlessMark: A Blind Diagnostically-Lossless Watermarking Framework for Medical Applications Based on Deep Neural Networks
Nowadays, with the development of public network usage, medical information
is transmitted throughout the hospitals. The watermarking system can help for
the confidentiality of medical information distributed over the internet. In
medical images, regions-of-interest (ROI) contain diagnostic information. The
watermark should be embedded only into non-regions-of-interest (NROI) to keep
diagnostic information without distortion. Recently, ROI based watermarking has
attracted the attention of the medical research community. The ROI map can be
used as an embedding key for improving confidentiality protection purposes.
However, in most existing works, the ROI map that is used for the embedding
process must be sent as side-information along with the watermarked image. This
side information is a disadvantage and makes the extraction process non-blind.
Also, most existing algorithms do not recover NROI of the original cover image
after the extraction of the watermark. In this paper, we propose a framework
for blind diagnostically-lossless watermarking, which iteratively embeds only
into NROI. The significance of the proposed framework is in satisfying the
confidentiality of the patient information through a blind watermarking system,
while it preserves diagnostic/medical information of the image throughout the
watermarking process. A deep neural network is used to recognize the ROI map in
the embedding, extraction, and recovery processes. In the extraction process,
the same ROI map of the embedding process is recognized without requiring any
additional information. Hence, the watermark is blindly extracted from the
NROI.Comment: Drs. Soroushmehr and Najarian declared that they had not
contributions to the paper. I removed their name
TRLF: An Effective Semi-fragile Watermarking Method for Tamper Detection and Recovery based on LWT and FNN
This paper proposes a novel method for tamper detection and recovery using
semi-fragile data hiding, based on Lifting Wavelet Transform (LWT) and
Feed-Forward Neural Network (FNN). In TRLF, first, the host image is decomposed
up to one level using LWT, and the Discrete Cosine Transform (DCT) is applied
to each 2*2 blocks of diagonal details. Next, a random binary sequence is
embedded in each block as the watermark by correlating coefficients. In
authentication stage, first, the watermarked image geometry is reconstructed by
using Speeded Up Robust Features (SURF) algorithm and extract watermark bits by
using FNN. Afterward, logical exclusive-or operation between original and
extracted watermark is applied to detect tampered region. Eventually, in the
recovery stage, tampered regions are recovered by image digest which is
generated by inverse halftoning technique. The performance and efficiency of
TRLF and its robustness against various geometric, non-geometric and hybrid
attacks are reported. From the experimental results, it can be seen that TRLF
is superior in terms of robustness and quality of the digest and watermarked
image respectively, compared to the-state-of-the-art fragile and semi-fragile
watermarking methods. In addition, imperceptibility has been improved by using
different correlation steps as the gain factor for flat (smooth) and texture
(rough) blocks