Multipurpose Image Watermarking: Ownership Check, Tamper Detection and Self-recovery

Sinhal, Rishi; Ansari, Irshad Ahmad

doi:10.1007/s00034-021-01926-z

Cited by 15 publications

(6 citation statements)

References 33 publications

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“…Our method uses VI and RI for dual tampering detection in each block, resulting in higher tampering detection accuracy compared to the single tampering detection approach of the other methods. Figure 11 shows the PSNR and SSIM comparison results between recovered images and cover images at different tampering rates compared to the methods of Tohidi [13], Aminuddin [17], Renklier [19], Haghighi [25], Li [26], Sinhal [27]. When the tampering rate is low, our method obtains higher PSNR and SSIM values.…”

Section: Tampering Detection and Recoverymentioning

confidence: 97%

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Reversible Image Fragile Watermarking with Dual Tampering Detection

Zhan,

Leng,

Chang

et al. 2024

Electronics

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The verification of image integrity has attracted increasing attention. Irreversible algorithms embed fragile watermarks into cover images to verify their integrity, but they are not reversible due to unrecoverable loss. In this paper, a new dual tampering detection scheme for reversible image fragile watermarking is proposed. The insect matrix reversible embedding algorithm is used to embed the watermark into the cover image. The cover image can be fully recovered when the dual-fragile-watermarked images are not tampered with. This study adopts two recovery schemes and adaptively chooses the most appropriate scheme to recover tampered data according to the square errors between the tampered data and the recovered data of two watermarked images. Tampering coincidence may occur when a large region of the fragile-watermarked image is tampered with, and the recovery information corresponding to the tampered pixels may be missing. The tampering coincidence problem is solved using image-rendering techniques. The experimental results show that the PSNR value of the watermarked image obtained using our scheme can reach 46.37 dB, and the SSIM value is 0.9942. In addition, high-accuracy tampering detection is achieved.

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Section: Tampering Detection and Recoverymentioning

confidence: 97%

“…Li et al [26] proposed a four-layer tampering detection algorithm to achieve high identification accuracy for the tampered region. Sinhal et al [27] embedded both a robust watermark and a fragile watermark in the cover image.…”

Section: Introductionmentioning

confidence: 99%

Reversible Image Fragile Watermarking with Dual Tampering Detection

Zhan,

Leng,

Chang

et al. 2024

Electronics

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“…Further, embed the watermark bit w r bit by adjusting the coefficient (C) values of LH2 and HL2 sub-bands as described in Eqs. ( 1) and ( 2) respectively [28]. Here, a is the embedding factor value, which can be adjusted to set the tradeoff line in between robustness and imperceptibility features.…”

Section: Robust Watermark Insertionmentioning

confidence: 99%

Machine learning based multipurpose medical image watermarking

Sinhal

Ansari

2023

Neural Comput & Applic

Self Cite

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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.

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“…With the rapid development of technologies such as the Internet, big data, and artificial intelligence, the financial technology service industry is undergoing unprecedented changes [1][2][3][4]. In this process, deep learning-based intelligent image recognition technology has attracted widespread attention as an important computer vision technology [5][6][7][8][9][10][11]. The financial technology service industry involves a large number of image and text information processing tasks, such as customer identity verification, loan approval, and credit evaluation.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Intelligent Image Recognition and Its Applications in Financial Technology Services

Wang¹,

Wang²,

Chang³

2023

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The financial technology service industry involves a large number of image and text information processing tasks. By automatically processing images and text information, financial institutions can greatly reduce labor costs, improve overall operational efficiency, and help financial institutions identify and predict risks more accurately, thereby improving risk management capabilities. The existing image symbol recognition and scene text detection methods may be affected in terms of recognition accuracy when processing complex scenes, low-resolution images or texts affected by obstacles, distortions and other factors. To this end, this study conducts an in-depth study on the application of deep learning-based intelligent image recognition in financial technology services. It elaborates the application scenarios of image symbol recognition and scene text detection in financial technology services. The ASTER model is improved, and the combination of attention mechanism sequential decoding can effectively capture local information and global dependencies in the feature sequence, thereby improving the recognition accuracy of the image symbol recognition model. By focusing on the center point position information of the text, pixels with the same center point are aggregated to reduce the interference between adjacent texts to some extent, achieving more accurate text segmentation. Experimental results validate the effectiveness of the method in this study.

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Multipurpose Image Watermarking: Ownership Check, Tamper Detection and Self-recovery

Cited by 15 publications

References 33 publications

Reversible Image Fragile Watermarking with Dual Tampering Detection

Reversible Image Fragile Watermarking with Dual Tampering Detection

Machine learning based multipurpose medical image watermarking

Deep Learning-Based Intelligent Image Recognition and Its Applications in Financial Technology Services

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