An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

Watermarks can provide reliable and secure copyright protection for optical coherence tomography(OCT)fundus images.The effective image segmentation is helpful for promoting OCT image watermarking.However,OCT images have a large amount of low-quality data,which seriously affects the performance of segmentationmethods.Therefore,this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network(RCNN).First,the rough-set-based feature discretization module is designed to preprocess the input data.Second,a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select important information for fusion.Finally,the refinement module for enhancing the extraction power of multi-scale information is added to improve the edge accuracy in segmentation.RCNN is compared with CE-Net and MultiResUNet on 83 gold standard 3D retinal OCT data samples.The average dice similarly coefficient(DSC)obtained by RCNN is 6%higher than that of CE-Net.The average 95 percent Hausdorff distance(95HD)and average symmetric surface distance(ASD)obtained by RCNN are 32.4%and 33.3%lower than those of MultiResUNet,respectively.We also evaluate the effect of feature discretization,as well as analyze the initial learning rate of RCNN and conduct ablation experiments with the four different models.The experimental results indicate that our method can improve the segmentation accuracy of OCT fundus images,providing strong support for its application in medical image watermarking.

Novel self-embedding holographic watermarking image encryption protection scheme

For digital image transmission security and information copyright,a new holographic image self-embedding watermarking encryption scheme is proposed.Firstly,the plaintext is converted to the RGB three-color channel,the corresponding phase hologram is obtained by holographic technology and the watermark is self-embedded in the frequency domain.Secondly,by applying the Hilbert transform principle and genetic center law,a complete set of image encryption algorithms is constructed to realize the encryption of image information.Finally,simulation results and security analysis indicate that the scheme can effectively encrypt and decrypt image information and realize the copyright protection of information.The introduced scheme can provide some support for relevant theoretical research,and has practical significance.

Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams,Lyapunov exponents(LEs),and bifurcation diagrams.Secondly,the parameter related firing behaviors are described through two-parameter bifurcation diagrams.Subsequently,local attraction basins reveal multi-stability phenomena related to initial values.Moreover,the proposed model is implemented on a microcomputer-based ARM platform,and the experimental results correspond to the numerical simulations.Finally,the article explores the application of digital watermarking for medical images,illustrating its features of excellent imperceptibility,extensive key space,and robustness against attacks including noise and cropping.

MarkNeRF:Watermarking for Neural Radiance Field

This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)models.We employ an embedding network to integrate the watermark into the images within the training set.Then,theNeRFmodel is utilized for 3Dmodeling.For copyright verification,a secret image is generated by inputting a confidential viewpoint into NeRF.On this basis,design an extraction network to extract embedded watermark images fromconfidential viewpoints.In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s copyright.The experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.

Improving Video Watermarking through Galois Field GF(2^(4)) Multiplication Tables with Diverse Irreducible Polynomials and Adaptive Techniques

Video watermarking plays a crucial role in protecting intellectual property rights and ensuring content authenticity.This study delves into the integration of Galois Field(GF)multiplication tables,especially GF(2^(4)),and their interaction with distinct irreducible polynomials.The primary aim is to enhance watermarking techniques for achieving imperceptibility,robustness,and efficient execution time.The research employs scene selection and adaptive thresholding techniques to streamline the watermarking process.Scene selection is used strategically to embed watermarks in the most vital frames of the video,while adaptive thresholding methods ensure that the watermarking process adheres to imperceptibility criteria,maintaining the video's visual quality.Concurrently,careful consideration is given to execution time,crucial in real-world scenarios,to balance efficiency and efficacy.The Peak Signal-to-Noise Ratio(PSNR)serves as a pivotal metric to gauge the watermark's imperceptibility and video quality.The study explores various irreducible polynomials,navigating the trade-offs between computational efficiency and watermark imperceptibility.In parallel,the study pays careful attention to the execution time,a paramount consideration in real-world scenarios,to strike a balance between efficiency and efficacy.This comprehensive analysis provides valuable insights into the interplay of GF multiplication tables,diverse irreducible polynomials,scene selection,adaptive thresholding,imperceptibility,and execution time.The evaluation of the proposed algorithm's robustness was conducted using PSNR and NC metrics,and it was subjected to assessment under the impact of five distinct attack scenarios.These findings contribute to the development of watermarking strategies that balance imperceptibility,robustness,and processing efficiency,enhancing the field's practicality and effectiveness.

Digital Text Document Watermarking Based Tampering Attack Detection via Internet

Owing to the rapid increase in the interchange of text information through internet networks,the reliability and security of digital content are becoming a major research problem.Tampering detection,Content authentication,and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies.The authors’difficulties were tampering detection,authentication,and integrity verification of the digital contents.This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection(ADMDTW-TAD)via the Internet.The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document’s appropriate characteristics to embed larger watermark information.The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely.Once the watermark is embedded with no damage to the original document,it is then shared with the destination.The watermark extraction process is performed to get the original document securely.The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes,and the outcomes were inspected in terms of different measures.The simulation values indicated that the ADMDTW-TAD technique improved performance over other models.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

Robust Deep Image Watermarking:A Survey

In the era of internet proliferation,safeguarding digital media copyright and integrity,especially for images,is imperative.Digital watermarking stands out as a pivotal solution for image security.With the advent of deep learning,watermarking has seen significant advancements.Our review focuses on the innovative deep watermarking approaches that employ neural networks to identify robust embedding spaces,resilient to various attacks.These methods,characterized by a streamlined encoder-decoder architecture,have shown enhanced performance through the incorporation of novel training modules.This article offers an in-depth analysis of deep watermarking’s core technologies,current status,and prospective trajectories,evaluating recent scholarly contributions across diverse frameworks.It concludes with an overview of the technical hurdles and prospects,providing essential insights for ongoing and future research endeavors in digital image watermarking.

Robust blind image watermarking based on interest points

Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cropping attacks,rotation attacks,etc.We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further.First,to extract more sparse and stable interest points,we use the Superpoint algorithm for generation and design two steps to perform the screening procedure.We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability.The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework.Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks.We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks.Through extensive experimental results,we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image.Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.

A Dual Domain Robust Reversible Watermarking Algorithm for Frame Grouping Videos Using Scene Smoothness

The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos.Grounded in the H.264 video coding standard,the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel.Subsequently,it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information,enabling successful watermark extraction and lossless recovery of the original video content.Experimental results demonstrate the algorithm’s strong imperceptibility,with each embedded frame in the experimental videos achieving a mean peak signal-to-noise ratio of 49.3830 dB and a mean structural similarity of 0.9996.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 7.59%and 0.4%on average.At the same time,the proposed algorithm has strong robustness to both offline and online attacks:In the face of offline attacks,the average normalized correlation coefficient between the extracted watermark and the original watermark is 0.9989,and the average bit error rate is 0.0089.In the face of online attacks,the normalized correlation coefficient between the extracted watermark and the original watermark is 0.8840,and the mean bit error rate is 0.2269.Compared with the three comparison algorithms,the performance of the two experimental indexes is improved by 1.27%and 18.16%on average,highlighting the algorithm’s robustness.Furthermore,the algorithm exhibits low computational complexity,with the mean encoding and the mean decoding time differentials during experimental video processing being 3.934 and 2.273 s,respectively,underscoring its practical utility.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

Reversible Semi-Fragile Watermarking Technique for Integrity Control of Relational Database

Reversible watermarking schemes for relational database are usually classified into two groups: robust schemes and fragile schemes. The main limitation of existing reversible fragile methods is that they cannot differentiate between legal and malicious modifications. In this paper, we introduce a novel lossless semi-fragile scheme based on prediction-error expansion for content protection of relational database. In the proposed method, all attributes in a database relation are first classified according to their sensitivity to legitimate updates. Then, the watermark is embedded by expanding the prediction error of the two least significant digits of securely selected attributes. At watermark extraction, the proposed method has the ability to fully restore the original data while detecting and localizing tampering. The applicability of our method is demonstrated theoretically and experimentally.

Wavelet-based gray-level digital image watermarking

The watermarking technique has been proposed as a method by hiding secret information into the image to protect the copyright of multimedia data. But most previous work focuses on the algorithms of embedding one dimensional watermarks or two dimensional binary digital watermarks. In this paper, a wavelet based method for embedding a gray level digital watermark into an image is proposed. By still image decomposition technique, a gray level digital watermark is decompounded into a series of bitplanes. By discrete wavelet transform ( DWT ), the host image is decomposed into multiresolution representations with hierarchical structure. The different bitplanes of the gray level watermark is embedded into the corresponding resolution of the decomposed host image. The experimental results show that the proposed techniques can successfully survive image processing operations and the lossy compression techniques such as Joint Photographic Experts Group (JPEG).

Efficient gray-level digital image watermarking based on vector quantization

Digital watermarking has been presented as a new method for copyright protection by embedding a secret signal in a digital image or video sequence. Common digital image watermarking techniques are based on the concept of spread spectrum communications, which can be classified in two catalogues: spatial domain and transform domain based. Most of transform domain watermarking methods are based on discrete cosine transforms (DCT) and robust to JPEG lossy compression. Recently, digital image watermarking based on another important lossy compression technique, vector quantization (VQ), has been presented, which carries watermark information by codeword indices. It is secret and efficient, and is robust to VQ compression with the same codebook. However, the embedded information is less and the extraction process requires the original image. This paper presents a more efficient VQ based image watermarking method, which can embed a large gray level watermark into the original image with less extra distortion and perform the watermark extraction without the original image. In addition, the proposed watermarking algorithm is very secret because two keys are required for watermark extraction. Experimental results demonstrate the effectiveness of the proposed technique.

Boosting image watermarking authenticity spreading secrecy from counting-based secret-sharing

This study presents enhancing images authentication by securing watermarking hidden data via shares generated from counting-based secret sharing.The trustfulness of shares utilised secret-sharing as an applicable privacy creation tool for the authentication of real-life complex platforms.This research adjusts embedding the watermarking data over the images by innovative redistribution of shares to be embedded spread over all the images.The anticipated watermarking technique guaranteed to scatter the share bits implanting at different least significant bits of image pixels as boosting up the trust overall authentication practicality.The paper experimentation performance analysis shows that this improved image watermarking authentication(capacity)is averagely better by 33%–67%than other related exclusive-OR oriented and octagon approaches.Interestingly,these measurement improvements did not degrade the robustness and security of the system,inspiring our research for opening novel track of related future counting-based secret-sharing authentication progresses to come.

New Fragile Watermarking Technique to Identify Inserted Video Objects Using H.264 and Color Features

The transmission of video content over a network raises various issues relating to copyright authenticity,ethics,legality,and privacy.The protection of copyrighted video content is a significant issue in the video industry,and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media.However,there are stillmany unresolved challenges.This paper aims to address those challenges by proposing a new technique for detectingmoving objects in digital videos,which can help prove the credibility of video content by detecting any fake objects inserted by hackers.The proposed technique involves using two methods,the H.264 and the extraction color features methods,to embed and extract watermarks in video frames.The study tested the performance of the system against various attacks and found it to be robust.The evaluation was done using different metrics such as Peak-Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index Measure(SSIM),Bit Correction Ratio(BCR),and Normalized Correlation.The accuracy of identifying moving objects was high,ranging from 96.3%to 98.7%.The system was also able to embed a fragile watermark with a success rate of over 93.65%and had an average capacity of hiding of 78.67.The reconstructed video frames had high quality with a PSNR of at least 65.45 dB and SSIMof over 0.97,making them imperceptible to the human eye.The system also had an acceptable average time difference(T=1.227/s)compared with other state-of-the-art methods.

Robust Watermarking Algorithm for Medical Volume Data Based on Polar Cosine Transform and 3D-DCT

The amount of 3D data stored and transmitted in the Internet of Medical Things(IoMT)is increasing,making protecting these medical data increasingly prominent.However,there are relatively few researches on 3D data watermarking.Moreover,due to the particularity of medical data,strict data quality should be considered while protecting data security.To solve the problem,in the field of medical volume data,we proposed a robust watermarking algorithm based on Polar Cosine Transform and 3D-Discrete Cosine Transform(PCT and 3D-DCT).Each slice of the volume data was transformed by PCT to obtain feature row vector,and then the reshaped three-dimensional feature matrix was transformed by 3D-DCT.Based on the contour information of the volume data and the detail information of the inner slice,the visual feature vector was obtained by applying the per-ceptual hash.In addition,the watermark was encrypted by a multi-sensitive initial value Sine and Piecewise linear chaotic Mapping(SPM)system,and embedded as a zero watermark.The key was stored in a third party.Under the same experimental conditions,when the volume data is rotated by 80 degrees,cut 25%along the Z axis,and the JPEG compression quality is 1%,the Normalized Correlation Coefficient(NC)of the extracted watermark is 0.80,0.89,and 1.00 respectively,which are significantly higher than the comparison algorithm.

A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

Medical images are a critical component of the diagnostic process for clinicians.Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis,they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images.Traditional watermark embedding algorithm embeds the watermark information into the medical image,which reduces the quality of the medical image and affects the physicians’judgment of patient diagnosis.In addition,watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed.This paper proposes a novel watermarking algorithm using the convolutional neural networks(CNN)Inception V3 and the discrete cosine transform(DCT)to address above mentioned problems.First,the medical image is input into the Inception V3 network,which has been structured by adjusting parameters,such as the size of the convolution kernels and the typical architecture of the convolution modules.Second,the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image.At last,the watermarks are encrypted using the logistic map system and hash function,and the keys are stored by a third party.The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark.In the experimental section,multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms.Our NC values for all the images are more than 90%accurate which shows the robustness of the algorithm.Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

Adaptive Reversible Visible Watermarking Based on Total Variation for BTC-Compressed Images

Few previous Reversible Visible Watermarking(RVW)schemes have both good transparency and watermark visibility.An adaptive RVW scheme that integrates Total Variation and visual perception in Block Truncation Coding(BTC)compressed domain,called TVB-RVW is proposed in this paper.A new mean image estimation method for BTC-compressed images is first developed with the help of Total Variation.Then,a visual perception factor computation model is devised by fusing texture and luminance characteristics.An adaptive watermark embedding strategy is used to embed the visible watermark with the effect of the visual perception factor in the BTC domain.Moreover,a lossless embedding method of the encrypted visible watermark is exploited to deter illegal watermark removal.The visible watermark can be removed since the visual perception factor and the estimated mean image remain unchanged before and after watermark embedding.Extensive experiments validate the superiority of the proposed algorithm over previous RVW schemes in BTC in terms of the visual quality of watermarked images and watermark visibility,and it can achieve a good balance between transparency and watermark visibility.