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. A 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) regions to keep diagnostically important details 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. Furthermore, a three-layer fully connected neural network is used for the detection of distorted NROI blocks in the recovery process to recover the distorted NROI blocks to their original form. The proposed framework is compared with one lossless watermarking algorithm. Experimental results demonstrate the superiority of the proposed framework in terms of side information.

     

Embedding in medical images with contrast enhancement and tamper detection capability

This paper presents an efficient data hiding technique capable of providing improved visual quality of watermarked images, besides having the ability to detect the tamper, if any. It is a spatial domain approach in which major emphasis is on improving the visual quality rather than increasing the PSNR or the embedding capacity. The medical images have been divided into Region of Interest (ROI) and Non-Region of Interest (NROI). Bringing out details that lie within the low dynamic range is very important in medical images for effective diagnosis. ROI being diagnostically critical region is enhanced using contrast stretching and subsequently, data is reversibly embedded into the peak bins of ROI. Only those peak bins are employed for reversible data embedding that have an adjacent empty bin to overcome the problem of overflow and underflow. In NROI, the uniform intensity and redundant information region, Least Significant Bit (LSB) embedding is employed for increasing the payload. For tamper detection, a fragile watermark has been embedded in the ROI. To evaluate the scheme various parameters like peak signal to noise ratio (PSNR), No-Reference Quality Metric for contrast-distorted images (NR-CDIQA) and Structural Similarity Index Matrix (SSIM) have been calculated. The experimental results show a remarkable increase in visual quality compared to state-of-art.

     

SVM based robust watermarking for enhanced medical image security

Medical images are more typical than any other ordinary images, since it stores patient’s information for diagnosis purpose. Such images need more security and confidentiality as total diagnosis depends on it. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data. The embedded data is scrambled using a unique key and then a transform domain based hybrid watermarking technique is used to embed the scrambled data into the transform coefficients of the host image. The data embedding in medical images involves more attention, so that the diagnosis part must not be affected by any modification. Therefore, Support Vector Machine (SVM) is used as a classifier, which classify a medical image into two regions i.e. Non Region of Interest (NROI) and Region of Interest (ROI) to embed watermark data into the NROI part of the medical image, using the proposed embedding algorithm. The objective of the proposed model is to avoid any quality degradation to the medical image. The simulation is performed to measure the Peak Signal to Noise Ratio (PSNR) for imperceptibility and Structural Similarity Index (SSIM) to test the robustness. The experimented result shows, robustness and imperceptibility with SSIM of more than 0.50 and PSNR of more than 35 dB for proposed watermarking model.

     

Information hiding in medical images: a robust medical image watermarking system for E-healthcare

Electronic transmission of the medical images is one of the primary requirements in a typical Electronic-Healthcare (E-Healthcare) system. However this transmission could be liable to hackers who may modify the whole medical image or only a part of it during transit. To guarantee the integrity of a medical image, digital watermarking is being used. This paper presents two different watermarking algorithms for medical images in transform domain. In first technique, a digital watermark and Electronic Patients Record (EPR) have been embedded in both regions; Region of Interest (ROI) and Region of Non-Interest (RONI). In second technique, Region of Interest (ROI) is kept untouched for tele-diagnosis purpose and Region of Non-Interest (RONI) is used to hide the digital watermark and EPR. In either algorithm 8?×?8 block based Discrete Cosine Transform (DCT) has been used. In each 8?×?8 block two DCT coefficients are selected and their magnitudes are compared for embedding the watermark/EPR. The selected coefficients are modified by using a threshold for embedding bit a ‘0’ or bit ‘1’ of the watermark/EPR. The proposed techniques have been found robust not only to singular attacks but also to hybrid attacks. Comparison results viz-a - viz payload and robustness show that the proposed techniques perform better than some existing state of art techniques. As such the proposed algorithms could be useful for e-healthcare systems.     

基于感兴趣区域的水印算法

在图像中,ROI（region of interest）所指的是图像的感兴趣区域,也就是图像中对于观察者而言信息最为丰富的地方。因而对于感兴趣区域的水印嵌入一直是数字水印研究的重点和难点。本文以基于小波变化和奇异值分解的水印为基础的嵌入方法,在感兴趣区域嵌入鲁棒性水印,用于作为图像的版权认证,使得水印具有较强的鲁棒性和不可见性。     

基于感兴趣区域的水印算法

在图像中,ROI(region of interest)所指的是图像的感兴趣区域,也就是图像中对于观察者而言信息最为丰富的地方.因而对于感兴趣区域的水印嵌入一直是数字水印研究的重点和难点.本文以基于小波变化和奇异值分解的水印为基础的嵌入方法,在感兴趣区域嵌入鲁棒性水印,用于作为图像的版权认证,使得水印具有较强的鲁棒性和不可见性.     

Hybrid watermarking of medical images for ROI authentication and recovery

Medical image data require strict security, confidentiality and integrity. To achieve these stringent requirements, we propose a hybrid watermarking method which embeds a robust watermark in the region of non-interest (RONI) for achieving security and confidentiality, while integrity control is achieved by inserting a fragile watermark into the region of the interest (ROI). First the information to be modified in ROI is separated and is inserted into RONI, which later is used in recovery of the original ROI. Secondly, to avoid the underflow and overflow, a location map is generated for embedding the watermark block-wise by leaving the suspected blocks. This avoids the preprocessing step of histogram modification. The image visual quality, as well as tamper localization, is evaluated. We use weighted peak signal to noise ratio for measuring image quality of watermarked images. Experimental results show that the proposed method outperforms the existing hybrid watermarking techniques.     

基于Tchebichef矩的几何攻击不变性第二代水印算法

提出了一种基于原始图像的Tchebichef矩实现的几何攻击不变性第二代盲水印算法,利用原始图像的Tchebichef矩估计图像可能经历的几何攻击的参数来还原图像,其中,原始图像的Tchebichef矩可作为水印检测器的密钥.水印嵌入过程结合人类视觉系统的特性,且可在任何图像变换域中实现,给出了小波域的一种实现方法.水印检测过程采用独立分量分析技术不仅可以检测到水印而且可以提取水印,实现了真正意义上的盲检测.实验结果表明,该水印算法对于通用水印测试软件Stirmark提供的各种攻击具有很好的鲁棒性.     

Watermarking of chest CT scan medical images for content authentication

Image processing techniques have played a very significant role in the past decades in the field of medical sciences for diagnosis and treatment purposes. In some applications, medical images are divided into region of interest (ROI) and region of non-interest (RONI). Important information regarding diagnosis is contained in the ROI, so its integrity must be assured. We propose a fragile watermarking technique to ensure the integrity of the medical image that avoids the distortion of the image in ROI by embedding the watermark information in RONI. The watermark is composed of patient information, hospital logo and message authentication code computed using a hash function. Earlier encryption of watermark is performed to ensure inaccessibility of embedded data to the adversaries.     

基于GHM和FCM的自适应图像水印算法

提出一种基于GHM多小波变换域上的模糊自适应水印算法。利用多小波系数块的能量、重要系数对小波块进行分类,结合图像自身的局部特性,在多小波系数中的两个相关的细节子图像中自适应地嵌入一幅二值水印图像;为了增强水印的鲁棒性,采用冗余嵌入方法;水印的提取不需要原始图像。实验结果证明,该算法对剪切、JPEG压缩、锐化等多种图像处理具有很好的鲁棒性。     

基于无损水印的医学图像篡改检测和高质量恢复

目的 采用无损数字水印算法对医学图像进行篡改检测和恢复是一个重要的研究领域。针对现有方法在区域划分和块特征值选取上的不足,提出一种新的基于四叉树分解和线性加权插值技术的无损水印算法。方法 首先对原始的医学图像进行四叉树分解,得到非固定尺寸且具有高同质性的图像块;然后利用线性加权插值方法计算每个图像块的特征值作为水印信息,最后采用基于混沌的简单可逆整数变换进行水印嵌入。结果 在提取端当水印图像没有受到篡改时,原始的图像能被无损恢复;当受到篡改时,算法能精确定位篡改区域并能高质量恢复,采用本文算法恢复的图像质量较现有方法高出20 dB左右。另外,在水印图像遭到较大程度篡改时,本文算法的正检率和负检率均优于现有方法。结论 实验结果表明,本文算法相比现有方法具有更高的嵌入容量、篡改检测精确性、恢复图像质量。算法适用于医学图像的完整性认证和篡改检测中。     

实离散分数Krawtchouk变换及其在数字图像水印中的应用

目的传统Krawtchouk变换处理图像时存在变换域信息表示单一、不可调节等问题,传统分数阶Krawtchouk变换处理实信号表示有冗余等问题。为了构造更加灵活的图像变换域表示,提出了实离散分数阶Krawtchouk变换,并应用于数字图像水印。方法利用对传统Krawtchouk变换矩阵特征值分解的方式,通过对分解后的特征值矩阵的实矩阵分数化构造得到了实离散分数阶Krawtchouk变换的变换矩阵,从而构造出实离散分数阶Krawtchouk变换。然后,利用所提变换从实数域变换到实数域这一特性,提出了在实离散分数阶Krawtchouk变换嵌入水印信息的图像水印算法。算法采用了图像分块处理的方式,对每个图像块的实离散分数Krawtchouk变换系数进行奇异值分解,将水印信息嵌入奇异值分解矩阵中,然后进行逆向实离散分数阶Krawtchouk变换得到嵌入水印后的图像。结果通过比较所提实离散分数阶Krawtchouk变换域水印算法和传统Krawtchouk变换域水印算法,提取水印的平均错误率在中值滤波、均值滤波、高斯滤波、JPEG压缩和缩放攻击下,分别降低了12.39%、10.04%、18.50%...     

Partially compressed-encrypted domain robust JPEG image watermarking

Digital media is often handled in a compressed and encrypted form in Digital Asset Management Systems. And watermarking of the compressed encrypted media items in the compressed-encrypted domain itself is required sometimes for copyright violation detection or other purposes. In this paper, we propose a robust image watermarking technique for partially compressed-encrypted JPEG images. However, arbitrary embedding of a watermark in a partially compressed encrypted image can cause drastic degradation of the quality as the underlying change may result in random decrypted values. In addition, due to the encryption the compression efficiency may become very low. Thus the challenge is to design a watermarking technique that provides good watermarked image quality and at the same time gives good compression efficiency. While the proposed technique embeds watermark in the partially compressed-encrypted domain, the extraction of watermark can be done in the encrypted or decrypted domains. The experiments show that the watermarked image quality is good and the reduction in compression efficiency is low. The proposed watermarking technique is robust to common signal processing attacks. The watermark detection performance of the proposed scheme is better than the existing encrypted domain watermarking techniques.     

基于局部相关匹配的小波变换图像水印方法*

现有局部感兴趣区域的水印嵌入方法在选取感兴趣区域时未考虑水印信号能与载体图像局部相似的特性,本文提出一种基于局部相关匹配的小波变换图像水印方法。该方法利用Arnold变换对水印图像加密,采用相位相关法选取合适的水印嵌入区域,并根据图像纹理的差异在小波的高频和低频系数上分别嵌入水印信息。水印检测时,在嵌入水印的区域提取出多个水印信息,将多个水印信息的加权平均作为检测水印。实验结果表明,该方法嵌入水印的不可见性及抗噪声和压缩的性能较好。     

基于扩频技术的音频水印处理算法的研究

目前的数字水印算法,主要集中在针对图像和视频,对音频、语音数据产品的数字水印算法研究仍然较少,需要提出更多的有效的针对音频、语音数据的数字水印算法。该文以二值图像作为数字水印信息,以音频为嵌入对象,在离散傅立叶变换域的系数上嵌入水印。在离散余弦变换域,采用扩频技术通过对变换域系数进行量化来嵌入水印信息,在水印的提取过程中不需要原始音频信号的参与。     

硬C-means聚类和DT-CWT变换的数字图像水印算法

为了提高数字水印图像的鲁棒性,提出一种基于硬C均值聚类和双树复小波变换域的图像水印算法。该算法对载体图像进行一层双树复小波变换分解,利用人类视觉特性对其2个低频子带进行硬C均值聚类划分,确定可嵌入信息区域。将二值水印图像信号经过Hilbert曲线置乱和降维,形成一维信号序列。利用图像自身局部相关性,调节水印嵌入强度并修改小波系数值,实现将水印嵌入到可嵌入信息区域。实验表明,该算法具有良好的透明性且对压缩、剪切、噪声和滤波等几何攻击具有高鲁棒性。     

基于ROI的组合域数字水印算法

提出一种基于ROI的组合域数字水印算法。将水印信号中感兴趣部分提取作为重要信息,混沌加密后嵌入载体图像的DCT域。将原始水印信号经过Arnold变换,嵌入含水印图像空域的非LSB位。水印提取时不需要原始载体图像。     

基于PDF417二维条码的顽健数字水印方法

利用PDF417二维条码其条块边角的特点,提出了一种基于条块边角的PDF417条码水印方法。首先对水印信息整体进行预判,以选择合适的黑白交界区域作为水印嵌入位置,根据水印内容对二维码图像的相关信息进行修改,以实现水印的嵌入和提取。实验结果表明,此算法相比同类空间域水印算法,具有更大的水印嵌入容量和更高的图像品质。同时,具有较好的抗JPEG压缩、高斯噪声和打印扫描功能,具有较强的顽健性。     

基于JPEG2000感兴趣区域自适应水印算法

提出一个基于JPEG2000感兴趣区域(ROI)的自适应水印算法。该算法结合了感兴趣区域的编码特点及HVS特性,在量化的ROI区域中筛选高频系数实现水印嵌入,使嵌入图保持良好的视觉效果,也使嵌入的水印信息在经历了JPEG2000压缩后仍具有较强的鲁棒性。     

Visible watermarking based on importance and just noticeable distortion of image regions

Visible watermarking is the process of embedding data (watermark) into a multimedia object (video/image) such that the embedded watermark is perceptible to a human observer. Many times, visible watermarks occlude important portion of multimedia objects. This paper introduces a visible watermarking algorithm to embed a binary logo watermark at N non-overlapping positions in an image such that important portions of the image are not occluded. The important portions are found through visual saliency computation or available human eye fixation density maps. In the proposed visible watermarking, just noticeable distortion is used to adaptively filter the watermark embedding energy based on the image content. A mathematical model in terms of information-content-weighted-structural-similarity-index and visual importance is proposed to find optimal watermark embedding strength. We tested the algorithm on several color images of different sizes and on several binary watermarks of different sizes and found the results to be very promising as per the requirements in visible watermarking. When compared to the state-of-the-art, we also found that the proposed technique does better in not hiding the details of any test image.