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

Zhang, Wenxing; Li, Jingbing; Bhatti, Uzair Aslam; Liu, Jing; Zheng, Jian; Chen, Yen‐Wei

doi:10.32604/cmc.2023.036317

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…The algorithm is resistant to a wide range of geometric attacks but is less resistant to conventional attacks [29]. Wenxing, Zhang et al proposed a method to train the GoogLeNet network using migration learning, and the trained network is used to extract the image features and encrypts the watermark using two-dimensional Henon chaos cryptography, and the proposed GoogLeNet-DCT algorithm has strong resistance to geometric attacks [30].…”

Section: Introductionmentioning

confidence: 99%

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

et al. 2023

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In the continuous progress of mobile internet technology, medical image processing technology is also always being upgraded and improved. In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. This paper offers a robustness zero watermarking strategy for medical pictures based on an Improved NasNet-Mobile convolutional neural network and the discrete cosine transform (DCT) to address the lack of robustness of existing medical image watermarking algorithms. First, the structure of the pre-training network NasNet-Mobile is adjusted by using a fully connected layer with 128 output and a regression layer instead of the original Softmax layer and classification layer, thus generating a regression network with 128 output, whereby the 128 features are extracted from the medical images using the NasNet-Mobile network with migration learning. Migration learning is then performed on the modified NasNet-Mobile network to obtain the trained network, which is then used to extract medical image features, and finally the extracted image features are subjected to DCT transform to extract low frequency data, and the perceptual hashing algorithm processes the extracted data to obtain a 32-bit binary feature vector. Before performing the watermark embedding, the watermark data is encrypted using the chaos mapping algorithm to increase data security. Next, the zero watermarking technique is used to allow the algorithm to embed and extract the watermark without changing the information contained in the medical image. The experimental findings demonstrate the algorithm’s strong resistance to both conventional and geometric assaults. The algorithm offers some practical application value in the realm of medicine when compared to other approaches.

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Section: Introductionmentioning

confidence: 99%

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

et al. 2023

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DeepSegRecycle: Deep Learning and ImageProcessing for Automated Waste Segregation and Recycling

Forhad,

Jahan,

Biswas

et al. 2024

2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

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An Epilepsy Diagnosis Method Based on a Transferred Alexnet Model and Eeg Signals

ZHANG,

LI,

DING

2024

J. Mech. Med. Biol.

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Epilepsy is a neurological disorder, and its sudden seizures pose a serious threat to the quality of life of patients. Not only does this condition cause patients to potentially lose control and consciousness during seizures, leading to possible injuries or dangerous situations, but it also has a significant impact on their mental health, triggering issues such as anxiety and depression. An intelligent epilepsy diagnosis process based on electroencephalogram (EEG) signals offers notable advantages. First, it provides noninvasive brainwave signals that accurately monitor and record the patterns and characteristics of epileptic seizures, offering objective diagnostic criteria for doctors. Second, with the use of artificial intelligence and machine learning technologies, large amounts of EEG data can be efficiently analyzed, improving the speed and accuracy of diagnosis and providing timely and effective treatment plans for patients. Additionally, intelligent diagnostic systems can achieve real-time monitoring, promptly alert people to potential epileptic seizures, and provide a safer living environment for patients. In this context, this paper proposes an epilepsy diagnosis method based on a transferred AlexNet model and EEG signals. The main contributions of this paper are as follows. (1) A transfer learning mechanism is incorporated into the AlexNet model through the direct transfer of its neural network structure and the modification of some existing neural network structures, followed by collaborative training with the addition of a domain adaptation layer in the network. This introduced transfer mechanism can address small sample size issues. (2) The traditional AlexNet model suffers from redundant feature extraction, leading to slow training. This paper adds batch normalization (BN) layers after each convolutional layer in the AlexNet model to normalize the features extracted from the convolutional layers. This emphasizes the representation of the important features of EEG signals and enables the lower layers of the network to learn the features needed for EEG signal processing. (3) The transferred AlexNet model proposed in this paper is applied to extract the features of epilepsy EEG signals, and the extracted features are input into a support vector machine (SVM) classifier to obtain epilepsy diagnosis results. Comparative experiments show that the diagnostic method used in this paper yields superior results and shorter training times than those of the competing approaches.

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

Cited by 5 publications

References 32 publications

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

Robust Zero Watermarking Algorithm for Medical Images Based on Improved NasNet-Mobile and DCT

DeepSegRecycle: Deep Learning and ImageProcessing for Automated Waste Segregation and Recycling

An Epilepsy Diagnosis Method Based on a Transferred Alexnet Model and Eeg Signals

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