Survey on watermarking methods in the artificial intelligence domain and beyond

Amrit, Preetam; Singh, Amit Kumar

doi:10.1016/j.comcom.2022.02.023

Cited by 48 publications

(17 citation statements)

References 56 publications

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“…Furthermore, once data are decrypted, several security questions arise. Watermarking is a popular solution to handling the security issue of any encryption scheme [ 7 ]. It involves embedding a secret mark in the host media as a form of identification [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

WatMIF: Multimodal Medical Image Fusion-Based Watermarking for Telehealth Applications

Singh

et al. 2022

Cogn Comput

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Over recent years, the volume of big data has drastically increased for medical applications. Such data are shared by cloud providers for storage and further processing. Medical images contain sensitive information, and these images are shared with healthcare workers, patients, and, in some scenarios, researchers for diagnostic and study purposes. However, the security of these images in the transfer process is extremely important, especially after the COVID-19 pandemic. This paper proposes a secure watermarking algorithm, termed WatMIF, based on multimodal medical image fusion. The proposed algorithm consists of three major parts: the encryption of the host media, the fusion of multimodal medical images, and the embedding and extraction of the fused mark. We encrypt the host media with a key-based encryption scheme. Then, a nonsubsampled contourlet transform (NSCT)-based fusion scheme is employed to fuse the magnetic resonance imaging (MRI) and computed tomography (CT) scan images to generate the fused mark image. Furthermore, the encrypted host media conceals the fused watermark using redundant discrete wavelet transform (RDWT) and randomised singular value decomposition (RSVD). Finally, denoising convolutional neural network (DnCNN) is used to improve the robustness of the WatMIF algorithm. The simulation experiments on two standard datasets were used to evaluate the algorithm in terms of invisibility, robustness, and security. When compared with the existing algorithms, the robustness is improved by 20.14%. Overall, the implementation of proposed watermarking for hiding fused marks and efficient encryption improved the identity verification, invisibility, robustness and security criteria in our WatMIF algorithm.

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Section: Introductionmentioning

confidence: 99%

WatMIF: Multimodal Medical Image Fusion-Based Watermarking for Telehealth Applications

Singh

et al. 2022

Cogn Comput

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“… [ 2 ] Blind watermarking targets the recovery of the watermark when the host is not available during the detection stage Discrete Shearlet Transform (DST), Discrete Wavelet Transform (DWT), Contourlet Transform (CT), Laplacian distribution Host images of size 512 × 512: Baboon, Barbara, Clock, Cameraman, Pepper, Boat Peak signal to noise ratio (PSNR), Structural similarity index measurement (SSIM), Root mean square error (RMSE) – The DST-based embedding provides a good imperceptibility and an improved payload, and the results demonstrate superior robustness against common image processing manipulations compared to DWT and CT [ 3 ] To get high robustness against noise addition attacks Discrete cosine transform (DCT), Discrete wavelet transform (DWT), Arnold cat map Host image of size 512 × 512: Barbara, Baboon, Pepper; Watermark logo: 32 × 32 binary image Mean absolute error (MAE), Peak Signal to noise ratio (PSNR) Salt and pepper noises, Cropping, JPEG compression, Gaussian low pass filter, Scaling The proposed method has high imperceptibility and robustness against different types of attacks. [ 4 ] This paper proposed a survey on watermarking methods in the artificial intelligence domain and beyond. [ 5 ] Dual Watermarking for Security of COVID-19 Patient Record.…”

Section: Introductionmentioning

confidence: 99%

Robust, imperceptible and optimized watermarking of DICOM image using Schur decomposition, LWT-DCT-SVD and its authentication using SURF

Awasthi

Srivastava

2022

Multimed Tools Appl

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In this proposed work, a dual image watermarking algorithm is used to protect the data against copyright violations. In this work, the DICOM image is used as a host image. Two watermark images used are the MNNIT logo and the personal data of the patient. This method utilizes the advantages of Schur decomposition, lifting wavelet transform (LWT), discrete cosine transform (DCT) and singular value decomposition (SVD). The scaling factor is a vital parameter of watermarking technique. The firefly optimization technique is used to get the optimized scaling factor. The Speeded-up robust features (SURF) are used for watermarking authentication. To evaluate the performance of the proposed algorithm, peak signal-to-noise ratio (PSNR), normalized correlation coefficient (NCC), and structural similarity index measurement (SSIM) are used. The proposed method is tested against various attacks such as Salt and Pepper noise, Gaussian noise, Gaussian low pass filter, Average filter, Median filter, Histogram equalization, Sharpening, Rotation and Region of interest filtering. The proposed algorithm shows a high level of robustness and imperceptibility. It is found that the features of the input host image and the watermarked image are matching correctly on applying the SURF technique.

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“…In addition to the classical watermarking schemes, deep-learning-based methods have been widely studied in recent years, achieving an outstanding performance compared with classical methods. 1,10,11 The key advantages to consider when selecting deep learning for watermarking are 1,12 (a) watermark generation for robust watermarking, (b) finding the ideal embedding position in the cover media, (c) identifying the best embedding strength that efficiently offers a balanced trade-off between quality and robustness, (d) offering attack simulation for efficient watermark extraction, and (e) reducing errors and denoising for obtained watermarks. However, the deep-learning model also faces the challenges of model security and privacy.…”

Section: Introductionmentioning

confidence: 99%

“…In the last few years, various papers containing the survey on digital media protection, devices, and artificial model protection using watermarking techniques have been published. 1,10,14,15 In Ref. 1, the authors summarized the roles and usage of deep-learning models in the different phases of the watermarking techniques.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive review of watermarking techniques in deep-learning environments

Singh

2022

J. Electron. Imag.

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Recently, the demand for the generation, sharing, and storage of massive amounts of multimedia information-especially in the form of images-from different intelligent devices and sensors has increased drastically. This introduces issues including the illegal access and fraudulent usage of this information as well as other security concerns. Watermarking consists of embedding a watermark design in a digital cover and then later extracting it to provide a solution for ownership conflict and copyright violation issues involving the media data. Presently, in watermarking, the use of deep-learning approaches is incredibly beneficial due to their accuracy, superior results and strong learning ability. We present a comprehensive review of watermarking techniques in deep-learning environments. We start with basic concepts of traditional and learning-based digital watermarking; we later review the popular deep-learning model-based digital watermarking methods; then, we summarize and compare the most recent contribution in the literature; finally, we highlight obfuscation challenges and further research directions.

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Survey on watermarking methods in the artificial intelligence domain and beyond

Cited by 48 publications

References 56 publications

WatMIF: Multimodal Medical Image Fusion-Based Watermarking for Telehealth Applications

WatMIF: Multimodal Medical Image Fusion-Based Watermarking for Telehealth Applications

Robust, imperceptible and optimized watermarking of DICOM image using Schur decomposition, LWT-DCT-SVD and its authentication using SURF

Comprehensive review of watermarking techniques in deep-learning environments

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