Robust medical zero‐watermarking algorithm based on Residual‐DenseNet

Gong, Cheng; Liu, Jing; Gong, Ming; Li, Jingbing; Bhatti, Uzair Aslam; Ma, Jixin

doi:10.1049/bme2.12100

Cited by 20 publications

(10 citation statements)

References 36 publications

(40 reference statements)

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“…While exhibiting strong robustness against translation and clipping, this scheme falls short of being an end-to-end solution. Regarding end-to-end zero-watermarking, some studies employ Convolutional Neural Networks (CNN), VGG-19 (developed by the Oxford Visual Geometry Group), or DenseNet to generate robust watermark sequences [27][28][29]. Another line of research predominantly revolves around the concept of style transfer [30].…”

Section: Zero-watermarkingmentioning

confidence: 99%

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

Li,

Sun,

Wang

et al. 2024

Applied Sciences

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In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces “ZWNet”, an end-to-end zero-watermarking scheme that obviates the necessity for specialized knowledge in image features and is exclusively composed of artificial neural networks. The architecture of ZWNet synergistically incorporates ConvNeXt and LK-PAN to augment the extraction of local features while accounting for the global context. A key aspect of ZWNet is its watermark block, as the network head part, which fulfills functions such as feature optimization, identifier output, encryption, and copyright fusion. The training strategy addresses the challenge of simultaneously enhancing robustness and discriminability by producing the same identifier for attacked images and distinct identifiers for different images. Experimental validation of ZWNet’s performance has been conducted, demonstrating its robustness with the normalized coefficient of the zero-watermark consistently exceeding 0.97 against rotation, noise, crop, and blur attacks. Regarding discriminability, the Hamming distance of the generated watermarks exceeds 88 for images with the same copyright but different content. Furthermore, the efficiency of watermark generation is affirmed, with an average processing time of 96 ms. These experimental results substantiate the superiority of the proposed scheme over existing zero-watermarking methods.

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Section: Zero-watermarkingmentioning

confidence: 99%

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

Li,

Sun,

Wang

et al. 2024

Applied Sciences

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“…In the presented TSODL-VD technique, the Residual-DenseNet model is applied for feature vector generation. An input of Residual-DenseNet has medicinal images, and the outcome is the vigorous Feature Vector (FV) of images [32]. The Residual-DenseNet is separated into two parts the backbone Network utilized for extracting image feature mapping previously Feature Output Element and the feature output element that procedures the feature mapping resultant by backbone networking.…”

Section: A Feature Extraction Modulementioning

confidence: 99%

Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems

Aldehim,

Asiri,

Aljebreen

et al. 2023

IEEE Access

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In smart video surveillance systems, violence detection becomes challenging to ensure public safety and security. With the proliferation of surveillance cameras in public areas, there is an increasing need for automated algorithms that can accurately and efficiently detect violent behavior in real time. This article presents a Tuna Swarm Optimization with Deep Learning Enabled Violence Detection (TSODL-VD) technique to classify violent actions in surveillance videos. The TSODL-VD technique enables the recognition of violence and can be a measure to avoid chaotic situations. In the presented TSODL-VD technique, the residual-DenseNet model is applied for feature vector generation from the input video frames and then passed into the stacked autoencoder (SAE) classifier. The SAE model is enforced to recognize the events into violence and non-violence events. To improve the violence detection effectiveness of the TSODL-VD procedure, the TSO protocol is utilized as a hyperparameter optimizer for the residual-DenseNet model. The performance validation of the TSODL-VD procedure has experimented on a benchmark violence dataset. The experimental results demonstrate that the TSODL-VD technique accomplishes precise and rapid detection outcomes over the recent state-of-the-art approaches.

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“…Authors of Ref. [6] proposed the method for Zero Watermarking with DCT and Residual DenseNet. Their proposed framework was robust against various geometric attacks.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Learning based Zero Watermarking for Authentication of Medical Records

Kaur,

Gupta,

Anand

2023

Soft Computing, Artificial Intelligence and Applications

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The security of digital images is crucial since they often contain sensitive and confidential data. Unauthorized access to this data could result in severe penalties for the parties involved. Despite the availability of highly secure algorithms, security remains a significant concern due to the rapid emergence of new technologies that can breach it. Thus the proposed work implements a technique that makes the confidential data inaccessible to intruders. Hence fragile type of data hiding technique is used where even with the slightest tampering to the image by an attacker, the information i.e. watermark image is completely destroyed, hence preventing it from unauthorized access. Also, a hybrid transform including DTCWT and NSST is used to fuse two medical images to form a more sophisticated output image, which serves as the final watermark. Further, the zero watermarking model is implemented using the ResNet 50 DL model for more precise results and extraction of feature maps. Embedding the actual image in the carrier image could make the watermarking detectable especially when it is fragile, hence Zero Watermarking overcomes this also by virtual embedding. Moreover, the algorithm employs the avalanche effect of SHA512 for highly secure authentication, further strengthening the security of the system. Overall, the proposed method is an effective way to ensure the security of digital images with confidential data.

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Robust medical zero‐watermarking algorithm based on Residual‐DenseNet

Cited by 20 publications

References 36 publications

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

Tuna Swarm Algorithm With Deep Learning Enabled Violence Detection in Smart Video Surveillance Systems

Deep Learning based Zero Watermarking for Authentication of Medical Records

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