Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Yang, Chengshan; Li, Jingbing; Bhatti, Uzair Aslam; Liu, Jing; Ma, Jixin; Huang, Mengxing

doi:10.1155/2021/4944797

Cited by 12 publications

(12 citation statements)

References 23 publications

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“…The comparison results are shown in Table 15. As can be seen from the table, for nongeometric attacks, such as Gaussian noise and JPEG compression (the attack intensity of the two is 5%), the performance of the proposed algorithm is slightly lower than that of the algorithm proposed by others in Table 15 [29][30][31][32], but the NC value of the two kinds of attacks is more than 0.5, which shows that the algorithm is robust.…”

Section: Translation Attackmentioning

confidence: 86%

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Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

Sheng¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

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Medical images are used as a diagnostic tool, so protecting their confidentiality has long been a topic of study. From this, we propose a Resnet50-DCT-based zero watermarking algorithm for use with medical images. To begin, we use Resnet50, a pre-training network, to draw out the deep features of medical images. Then the deep features are transformed by DCT transform and the perceptual hash function is used to generate the feature vector. The original watermark is chaotic scrambled to get the encrypted watermark, and the watermark information is embedded into the original medical image by XOR operation, and the logical key vector is obtained and saved at the same time. Similarly, the same feature extraction method is used to extract the deep features of the medical image to be tested and generate the feature vector. Later, the XOR operation is carried out between the feature vector and the logical key vector, and the encrypted watermark is extracted and decrypted to get the restored watermark; the normalized correlation coefficient (NC) of the original watermark and the restored watermark is calculated to determine the ownership and watermark information of the medical image to be tested. After calculation, most of the NC values are greater than 0.50. The experimental results demonstrate the algorithm's robustness, invisibility, and security, as well as its ability to accurately extract watermark information. The algorithm also shows good resistance to conventional attacks and geometric attacks.

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Section: Translation Attackmentioning

confidence: 86%

“…For geometric attacks, when the rotation angle reaches 10°, the NC value can reach 0.88 respectively, while the NC value of the algorithm [29,30] is 0.82 and 0.61 respectively. When the rotation angle reaches 20°, the NC values of the algorithm [29,30] are 0.79 and 0.53 respectively.…”

Section: Translation Attackmentioning

confidence: 99%

Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

Sheng¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

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“…In this study, we deal only with the watermark embedding part and not with the extraction part since we study the performance of the models on watermarked images. There are numerous watermarking methodologies, like other moment families [ 33 ] or transformations [ 34 ], that are applied in medical images, and these can constitute a new category of attacks.…”

Section: Methodsmentioning

confidence: 99%

Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning

Apostolidis

Papakostas

2022

J. Imaging

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In the past years, Deep Neural Networks (DNNs) have become popular in many disciplines such as Computer Vision (CV), and the evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to deal with several problems. One of the most important challenges in the CV area is Medical Image Analysis. However, adversarial attacks have proven to be an important threat to vision systems by significantly reducing the performance of the models. This paper brings to light a different side of digital watermarking, as a potential black-box adversarial attack. In this context, apart from proposing a new category of adversarial attacks named watermarking attacks, we highlighted a significant problem, as the massive use of watermarks, for security reasons, seems to pose significant risks to vision systems. For this purpose, a moment-based local image watermarking method is implemented on three modalities, Magnetic Resonance Images (MRI), Computed Tomography (CT-scans), and X-ray images. The introduced methodology was tested on three state-of-the art CV models, DenseNet 201, DenseNet169, and MobileNetV2. The results revealed that the proposed attack achieved over 50% degradation of the model’s performance in terms of accuracy. Additionally, MobileNetV2 was the most vulnerable model and the modality with the biggest reduction was CT-scans.

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“…In the experimental process, we selected the same size of brain map and text "HN" watermark information for testing to maintain uniformity. Then, we compared with the existing algorithms KAZE-DCT [30], Zernike-DCT [31], Inception V3-DCT [32], and DWT-DCT. As can be seen, from both Table 6 and Fig.…”

Section: Algorithms Comparisonmentioning

confidence: 99%

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

Zhang¹,

Li²,

Bhatti³

et al. 2023

Computers, Materials &Amp; Continua

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The field of medical images has been rapidly evolving since the advent of the digital medical information era. However, medical data is susceptible to leaks and hacks during transmission. This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage, as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms. First, a pre-trained GoogLeNet network is used in this paper, based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset, so that the pre-trained network can further learn the deep convolutional features in the medical dataset, and then the trained network is used to extract the stable feature vectors of medical images. Then, a two-dimensional Henon chaos encryption technique, which is more sensitive to initial values, is used to encrypt multiple different types of watermarked private information. Finally, the feature vector of the image is logically operated with the encrypted multiple watermark information, and the obtained key is stored in a third party, thus achieving zero watermark embedding and blind extraction. The experimental results confirm the robustness of the algorithm from the perspective of multiple types of watermarks, while also demonstrating the successful embedding of multiple watermarks for medical images, and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

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Robust Zero Watermarking Algorithm for Medical Images Based on Zernike-DCT

Cited by 12 publications

References 23 publications

Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning

Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

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