A robust video zero-watermarking based on deep convolutional neural network and self-organizing map in polar complex exponential transform domain

2020 ◽  
Author(s):  
Yumei Gao ◽  
Xiaobing Kang ◽  
Yajun Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Transform Domain ◽  
Self Organizing Map ◽  
Exponential Transform ◽  
Zero Watermarking ◽  
Polar Complex Exponential Transform ◽  
Complex Exponential ◽  
Self Organizing

scholarly journals A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wenbing Wang ◽  
Yan Li ◽  
Shengli Liu
Keyword(s):  
Medical Images ◽  
Computation Time ◽  
Image Features ◽  
Computational Time ◽  
Geometric Attacks ◽  
Exponential Transform ◽  
Zero Watermarking ◽  
Polar Complex Exponential Transform ◽  
The Stability ◽  
Complex Exponential

Zero-watermarking is one of the solutions for image copyright protection without tampering with images, and thus it is suitable for medical images, which commonly do not allow any distortion. Moment-based zero-watermarking is robust against both image processing and geometric attacks, but the discrimination of watermarks is often ignored by researchers, resulting in the high possibility that host images and fake host images cannot be distinguished by verifier. To this end, this paper proposes a PCET- (polar complex exponential transform-) based zero-watermarking scheme based on the stability of the relationships between moment magnitudes of the same order and stability of the relationships between moment magnitudes of the same repetition, which can handle multiple medical images simultaneously. The scheme first calculates the PCET moment magnitudes for each image in an image group. Then, the magnitudes of the same order and the magnitudes of the same repetition are compared to obtain the content-related features. All the image features are added together to obtain the features for the image group. Finally, the scheme extracts a robust feature vector with the chaos system and takes the bitwise XOR of the robust feature and a scrambled watermark to generate a zero-watermark. The scheme produces robust features with both resistance to various attacks and low similarity among different images. In addition, the one-to-many mapping between magnitudes and robust feature bits reduces the number of moments involved, which not only reduces the computation time but also further improves the robustness. The experimental results show that the proposed scheme meets the performance requirements of zero-watermarking on the robustness, discrimination, and capacity, and it outperforms the state-of-the-art methods in terms of robustness, discrimination, and computational time under the same payloads.

Robust zero-watermarking algorithm based on polar complex exponential transform and logistic mapping

2016 ◽  
Vol 76 (24) ◽  
pp. 26355-26376 ◽  
Author(s):  
Chun-peng Wang ◽  
Xing-yuan Wang ◽  
Xing-jun Chen ◽  
Chuan Zhang
Keyword(s):  
Logistic Mapping ◽  
Exponential Transform ◽  
Zero Watermarking ◽  
Polar Complex Exponential Transform ◽  
Complex Exponential

Novel quaternion polar complex exponential transform and its application in color image zero-watermarking

2021 ◽  
pp. 103130
Author(s):  
Zhiqiu Xia ◽  
Xingyuan Wang ◽  
Chunpeng Wang ◽  
Bin Ma ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Color Image ◽  
Exponential Transform ◽  
Zero Watermarking ◽  
Polar Complex Exponential Transform ◽  
Complex Exponential

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Deep Convolutional Neural Network applied to Chagas Disease Parasitemia Assessment

2020 ◽  
Author(s):  
André Pereira ◽  
Alexandre Pyrrho ◽  
Daniel Vanzan ◽  
Leonardo Mazza ◽  
José Gabriel Gomes
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Chagas Disease ◽  
Deep Convolutional Neural Network
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