Robust Image Watermarking Research Articles

A convolutional neural network-based blind robust image watermarking approach exploiting the frequency domain

A convolutional neural network-based blind robust image watermarking approach exploiting the frequency domain

Deep learning-based image watermarking technique with hybrid DWT-SVD

ABSTRACT In digital media copyright protection, image watermarking topologies afford a promising solution. But the robustness of watermarking methods should be considered. Therefore, a robust image watermarking technique has been proposed to show better robustness against rotation attacks and other issues in watermarking. In this work, the Deep Belief Network (DBN) is trained by the Bear Smell Search Algorithm (BSSA) during the first phase. The second phase is the embedding phase, which utilizes the hybrid transform domain of Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). After embedding, the extraction is performed with the help of a Back Propagation Neural Network (BPNN). The experiments are conducted on four kinds of host images such as Barbara, Peppers, Person and Lena. Experimental results proved that the superiority of our proposed method achieving the state-of-the-art accuracy.

Robust digital color image watermarking based on compressive sensing and DWT

Robust digital color image watermarking based on compressive sensing and DWT

Robust Image Watermarking in Spatial Domain Utilizing Features Equivalent to SVD Transform

In recent years, digital image watermarking has gained a significant amount of popularity and developed into a crucial and essential tool for copyright protection, security, and the identification of multimedia content. Despite its high computational complexity, singular value decomposition (SVD) is an extensively utilized transformation in digital image watermarking. This research presents a robust and blind image watermarking scheme that directly alters the image pixels in the spatial domain to incorporate the watermark by quantizing the block-wise invariant maximum singular value. Using a distribution rule, pixels from the cover image are redistributed to obtain a new image that is divided into square and non-overlapping blocks to obtain invariant maximum singular values by using the matrix 2-norm in the spatial domain without performing an SVD transform. This modifies the pixels of the cover image such that the outcome is equivalent to the difference between the maximum singular values of the corresponding blocks in covers and watermarked images. The strengths of the proposed approach are highlighted by a comparison of experimental results with the most recent and comparable watermarking approaches.

A novel robust blind medical image watermarking using GWO optimized DWT-DCT-SVD

A novel robust blind medical image watermarking using GWO optimized DWT-DCT-SVD

A novel robust blind medical image watermarking using rank-based DWT

A novel robust blind medical image watermarking using rank-based DWT

Machine learning based multipurpose medical image watermarking.

Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing operations. It is essential because the changed/manipulated data may lead to erroneous judgment by medical experts and can negatively influence the human's heath. This work offers a blind and robust medical image watermarking framework using deep neural network to provide effective security solutions for medical images. During watermarking, the region of interest (ROI) data of the original image is preserved by employing the LZW (Lampel-Ziv-Welch) compression algorithm. Subsequently the robust watermark is inserted into the original image using IWT (integer wavelet transform) based embedding approach. Next, the SHA-256 algorithm-based hash keys are generated for ROI and RONI (region of non-interest) regions. The fragile watermark is then prepared by ROI recovery data and the hash keys. Further, the LSB replacement-based insertion mechanism is utilized to embed the fragile watermark into RONI embedding region of robust watermarked image. A deep neural network-based framework is used to perform robust watermark extraction for efficient results with less computational time. Simulation results verify that the scheme has significant imperceptibility, efficient robust watermark extraction, correct authentication and completely reversible nature for ROI recovery. The relative investigation with existing schemes confirms the dominance of the proposed work over already existing work.

Block SMRT and knapsack optimization-based sequency selector for robust, imperceptible, and payload-efficient color image watermarking for binary watermark

Watermarking is a generic strategy for overcoming numerous issues associated with multimedia security. Performance of a watermarking system in terms of imperceptibility, robustness and payload is highly dependent on the positions used for embedding. The choice of sequency packets plays an important role in the performance when Sequency-based Mapped Real Transform (SMRT) is used for embedding. A novel sequency selector for robust, imperceptible and payload-efficient color image watermarking of binary watermark is proposed using SMRT and two-level optimization. SMRT of block partitioned Cb channel of cover image is input to sequency selector where results of first-level optimization, based on imperceptibility and robustness, along with payload is optimized using knapsack optimization in the second level. The binary watermark is LSB embedded in SMRT coefficients present in each packet of the optimal combination of sequencies chosen by the sequency selector. Simulation results show better performance compared to existing optimized and non-optimized embedding techniques.

A Highly Robust Medical Image Watermarking Method for Medical Real-time Applications.

Watermarking such as other security concepts is an ongoing challenging research issue, especially for medical images, to protect patient privacy. Medical images need to be shared and transferred between hospitals and specialists as quickly as possible for better diagnosis. Fast and simple watermarking is needed as well as the robust transferring of channel noise, such as salt and pepper noise and robust cropping that may occur from specialists and signature encryption for patient privacy. In this article, a highly robust and simple watermarking method is introduced. The proposed method has very low computational complexity and at the same time, it is very robust to interference and uses simple computations such as (XORs) Exclusive ORs and rotations that can be done in real-time. The proposed method uses a combination of hidden neighboring signature information, Sudoku permutation, and noise pre-processing to achieve high robustness against salt and pepper noise and cropping. Simple signature encryption is also used. The proposed method is examined in different medical image datasets. The experimental results indicate the proposed watermarking system is robust to salt and pepper noise density of up to 90% and about 70% cropping. The number of computations including encryption is five XOR per pixel and a rotation per block of signature size. A novel method for medical image watermarking is presented. The proposed method is in the spatial domain, has encryption, and uses only XOR computation. The proposed method is highly robust to noise and cropping which is necessary for medical uses. The proposed method can be used efficiently for real-time watermarking for medical and nonmedical image datasets.

A Robust Medical Image Watermarking Scheme Based on Nature-Inspired Optimization for Telemedicine Applications

Medical images and patient information are routinely transmitted to a remote radiologist to assist in diagnosis. It is critical in e-healthcare systems to ensure that data are accurately transmitted. Medical images of a person’s body can be used against them in many ways, including by transmitting them. Copyright and intellectual property laws prohibit the unauthorized use of medical images. Digital watermarking is used to prove the authenticity of the medical images before diagnosis. In this paper, we proposed a hybrid watermarking scheme using the Slantlet transform, randomized-singular value decomposition, and optimization techniques inspired by nature (Firefly algorithm). The watermark image is encrypted using the XOR encryption technique. Extensive testing reveals that our innovative approach outperforms the existing methods based on the NC, SSIM, and PSNR. The SSIM and NC values of watermarked image and extracted watermark are close to or equal to 1 at a scaling factor of 0.06, and the PSNR of the proposed scheme lies between 58 dB and 59 dB, which shows the better performance of the scheme.

CADW: CGAN-Based Attack on Deep Robust Image Watermarking

Robust watermarking plays an essential role in copyright protection for digital images. Meanwhile, the studies of robust image watermarking and corresponding attack methods promote and complement each other. Because the traditional attack methods are weak to attack the deep watermarking, in this work, we thus design an effective watermarking attack method based on the conditional generative adversarial nets (CGAN). Also, according to the network structure of CGAN, a targeted and combined loss function is constructed, which can guarantee acceptable visual quality of attacked image and make the watermark extraction fail simultaneously. Experimental results demonstrate that our method is effective for some state-of-the-art deep robust image watermarking methods with the transferability. In addition, as an attack method, it can be regarded as an evaluation standard to measure the robustness of watermarking.

Robust and Secure Medical Image Watermarking for Edge-Enabled e-Healthcare

Robust and Secure Medical Image Watermarking for Edge-Enabled e-Healthcare

A Robust Image Watermarking Scheme Based on Image Normalization and Contourlet Transform

A Robust Image Watermarking Scheme Based on Image Normalization and Contourlet Transform

ARWGAN: Attention-Guided Robust Image Watermarking Model Based on GAN

In the existing deep learning based watermarking models, extracted image features for fusing with watermark are not abundant enough and more critically, essential features are not highlighted to be learned with the purpose of robust watermarking, both of which limit the watermarking performance. To solve those two drawbacks, this paper proposes an attention-guided robust image watermarking model based on generative adversarial network (ARWGAN). To acquire a great deal of representational image features, a feature fusion module (FFM) is devised to learn shallow and deep features effectively for multi-layer fusion with watermark, and meanwhile, reuse of those features by the dense connection enhances robustness. To alleviate image distortion caused by embedding watermark, an attention module (AM) is deployed to compute the attention mask by mining the global features of the original image. Specifically, with the guidance of the attention mask, image features representing inconspicuous regions and texture regions are enhanced for embedding the high strength of watermark, and simultaneously other features are suppressed to improve the watermarking performance. Furthermore, the noise sub-network is adopted for robustness enhancement by simulating various image attacks in iterative training. The discriminator is used to distinguish the encoded image from the original image for improving watermarking invisibility continuously. Experimental results demonstrate that ARWGAN is superior to the existing state-of-the-art watermarking models, and ablation experiments prove the effectiveness of the FFM and the AM. The code is avaliable in https://github.com/river-huang/ARWGAN.

DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking

At present, deep learning has achieved excellent achievements in image processing and computer vision and is widely used in the field of watermarking. Attention mechanism, as the research hot spot of deep learning, has not yet been applied in the field of watermarking. In this paper, we propose a deep learning and attention network for robust image watermarking (DARI-Mark). The framework includes four parts: an attention network, a watermark embedding network, a watermark extraction network, and an attack layer. The attention network used in this paper is the channel and spatial attention network, which calculates attention weights along two dimensions, channel and spatial, respectively, assigns different weights to pixels in different channels at different positions and is applied in the watermark embedding and watermark extraction stages. Through end-to-end training, the attention network can locate nonsignificant areas that are insensitive to the human eye and assign greater weights during watermark embedding, and the watermark embedding network selects this region to embed the watermark and improve the imperceptibility. In watermark extraction, by setting the loss function, larger weights can be assigned to watermark-containing features and small weights to noisy signals, so that the watermark extraction network focuses on features about the watermark and suppresses noisy signals in the attacked image to improve robustness. To avoid the phenomenon of gradient disappearance or explosion when the network is deep, both the embedding network and the extraction network have added residual modules. Experiments show that DARI-Mark can embed the watermark without affecting human subjective perception and that it has good robustness. Compared with other state-of-the-art watermarking methods, the proposed framework is more robust to JPEG compression, sharpening, cropping, and noise attacks.

A MULTIPLE AND ROBUST IMAGE WATERMARKING APPROACH BASED ON LWT AND FWHT FOR MEDICAL DATA SECURITY

In the last years, the internet and multimedia technologies are widely used to exchange medical images and practicing telemedicine; which exposes this data to various illegitimate attacks due to their sensitivity. For this reason, many researchers work to afford a proper security and an efficient protection during the transmission. In this context, we propose a blind multi-watermarking approach for medical images using Lifting Wavelet Transform and Fast Walsh–Hadamard Transform. This approach proves the ability to embed a binary image ([Formula: see text]), used for integrity purposes, in addition to the patient information (EPR) associated with the medical cover image. Moreover, an error correcting code was used to improve the security of the EPR. We tested the proposed approach on different imaging modalities, and the obtained results show a good imperceptibility and robustness against several common attacks.

Cross-scale feature fusion-based JND estimation for robust image watermarking in quaternion DWT domain

Owing to the rapid development of just-noticeable difference (JND) and the high robustness of discrete wavelet transform (DWT) based watermarking, robust watermarking methods based on JND models have been extensively adopted in the area of image copyright protection. In general, in existing JND model-based methods, transform domain-based features play an important role during the quantization strength measurement. However, most existing methods only use the subband-specific features from the final multi-scale wavelet transform domain, ignoring the feature relevance among subband-specific features with different scales among RGB channels. To address this issue, in this study, we put forward a JND estimation based on cross-scale feature fusion (CFJnd) mechanism in quaternionic DWT domain for robust image watermarking. For the proposed CFJnd, we first design different quaternionic wavelet scales for the JND modeling, which can be used to measure the minimum distortion level of the embedding-specific subband relevance. Furthermore, the cross-scale features among different subbands are adopted to jointly generate the pattern, texture and color measurement, making the final perceptual masking more representative. In addition, we further develop a CFJnd-based robust image watermarking, and the watermark is embedded by quantifying the candidate quaternionic DWT coefficients of the embedding-specific subband on a level which is adaptively defined according to the proposed CFJnd. Experimental results based on the publicly available image database show that the proposed method can produce reliable and promising results, compared to other state-of-the-art image watermarking methods.

A Hyper-Chaotically Encrypted Robust Digital Image Watermarking Method with Large Capacity Using Compress Sensing on a Hybrid Domain.

The digital watermarking technique is a quite promising technique for both image copyright protection and secure transmission. However, many existing techniques are not as one might have expected for robustness and capacity simultaneously. In this paper, we propose a robust semi-blind image watermarking scheme with a high capacity. Firstly, we perform a discrete wavelet transformation (DWT) transformation on the carrier image. Then, the watermark images are compressed via a compressive sampling technique for saving storage space. Thirdly, a Combination of One and Two-Dimensional Chaotic Map based on the Tent and Logistic map (TL-COTDCM) is used to scramble the compressed watermark image with high security and dramatically reduce the false positive problem (FPP). Finally, a singular value decomposition (SVD) component is used to embed into the decomposed carrier image to finish the embedding process. With this scheme, eight grayscale watermark images are perfectly embedded into a carrier image, the capacity of which is eight times over that of the existing watermark techniques on average. The scheme has been tested through several common attacks on high strength, and the experiment results show the superiority of our method via the two most used evaluation indicators, normalized correlation coefficient (NCC) values and the peak signal-to-noise ratio (PSNR). Our method outperforms the state-of-the-art in the aspects of robustness, security, and capacity of digital watermarking, which exhibits great potential in multimedia application in the immediate future.

A highly robust and secure digital image watermarking using modified optimal foraging algorithm - based SVM classifier and hyperchaotic Fibonacci Q-matrix

Transmission media is at rise over the internet, due to which the copyright risk is alarming. In such situation, enhanced security is the main concern. Digital image watermarking is the technique that ensures the copyright protection, security, and authenticity of the digital image. This research work recommends a highly secured and robust digital image watermarking system. In this method, the cover image is confused by arbitrarily generated numbers by a six-dimensional hyperchaotic technique, then this permuted image is scattered by applying the Fibonacci Q matrix to generate a scattered host image. This scattered image is decomposed up to 3 levels through DWT and the low pass sub-band caused by DWT are further decomposed by SVD. Singular values generated by SVD are used for watermark embedding as a slight change in the value does not affect the image quality. SVM classifier is used to classify the appropriate location to insert the scattered binary watermark. In this method SVM parameters are optimized by a modified optimal foraging algorithm, so that classification error can be reduced. Pixel rearrangement of the watermark image and host image makes the proposed method more secure, and it is highly robust as SVM is trained to classify locations that are less distorted by noise. Experimental outcomes depicts that the proposed method is accurate and better to the current cutting-edge methods in terms of security and robustness of digital image watermarking, as PSNR is approx. 72db and NC values are 1 after applying all the possible attacks.

A New Robust and Secure 3-Level Digital Image Watermarking Method Based on G-BAT Hybrid Optimization

This contribution applies tools from the information theory and soft computing (SC) paradigms to the embedding and extraction of watermarks in aerial remote sensing (RS) images to protect copyright. By the time 5G came along, Internet usage had already grown exponentially. Regarding copyright protection, the most important responsibility of the digital image watermarking (DIW) approach is to provide authentication and security for digital content. In this paper, our main goal is to provide authentication and security to aerial RS images transmitted over the Internet by the proposal of a hybrid approach using both the redundant discrete wavelet transform (RDWT) and the singular value decomposition (SVD) schemes for DIW. Specifically, SC is adopted in this work for the numerical optimization of critical parameters. Moreover, 1-level RDWT and SVD are applied on digital cover image and singular matrices of LH and HL sub-bands are selected for watermark embedding. Further selected singular matrices SLH and SHL are split into 3×3 non-overlapping blocks, and diagonal positions are used for watermark embedding. Three-level symmetric encryption with low computational cost is used to ensure higher watermark security. A hybrid grasshopper–BAT (G-BAT) SC-based optimization algorithm is also proposed in order to achieve high quality DIW outcomes, and a broad comparison against other methods in the state-of-the-art is provided. The experimental results have demonstrated that our proposal provides high levels of imperceptibility, robustness, embedding capacity and security when dealing with DIW of aerial RS images, even higher than the state-of-the-art methods.