scholarly journals A Novel Image Tamper Detection and Self-Recovery Algorithm Based on Watermarking and Chaotic System

Mathematics ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 955 ◽  
Author(s):  
Yewen Li ◽  
Wei Song ◽  
Xiaobing Zhao ◽  
Juan Wang ◽  
Lizhi Zhao
Keyword(s):  
Chaotic System ◽  
Detection Algorithm ◽  
Tamper Detection ◽  
Original Image ◽  
Hidden Information ◽  
Image Block ◽  
Recovery Algorithm ◽  
Content Security ◽  
Recovery Block ◽  
Resistance Performance

With the development of image editing software techniques, the content integrity and authenticity of original digital images become more and more important in digital content security. A novel image tampering detection and recovery algorithm based on digital watermarking technology and a chaotic system is proposed, and it can effectively locate the tampering region and achieve the approximate recovery of the original image by using the hidden information. The pseudo-random cyclic chain is realized by the chaotic system to construct the mapping relationship between the image subblocks. It can effectively guarantee the randomness of the positional relationship between the hidden information and the original image block for the better ergodicity of the pseudo-random chain. The recovery value optimization algorithm can represent image information better. In addition to the traditional Level-1 recovery, a weight adaptive algorithm is designed to distinguish the original block from the primary recovery block, allowing 3 × 3 neighbor block recovery to achieve better results. The experimental results show that the hierarchical tamper detection algorithm makes tamper detection have higher precision. When facing collage attacks and large general tampering, it will have higher recovery image quality and better resistance performance.

scholarly journals Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard

2018 ◽  
Vol 8 (9) ◽  
pp. 1540 ◽  
Author(s):  
Xiaoqiang Zhang ◽  
Xuesong Wang
Keyword(s):  
Remote Sensing ◽  
Image Encryption ◽  
Chaotic System ◽  
Remote Sensing Image ◽  
Encryption Algorithm ◽  
Advanced Encryption Standard ◽  
Remote Sensing Images ◽  
Simulation Data ◽  
Content Security ◽  
Image Encryption Algorithm

With the increasing use of multimedia in communications, the content security of remote-sensing images attracts much attention in both the academia and industry. The Advanced Encryption Standard (AES) is a famous symmetric cryptosystem. A symmetric remote-sensing image encryption algorithm using AES is presented. Firstly, to reduce the encryption times, the sender groups 16 pixel values together, and converts them into big integers; secondly, the sender encrypts big integers with AES and the chaotic system; finally, the encrypted image is obtained from encrypted big integers. Simulation data show that our algorithm exhibits both the high security and efficiency.

Detection and Tracking Cows by Computer Vision and Image Classification Methods

2021 ◽  
Vol 13 (1) ◽  
pp. 1-45
Author(s):  
Terry Gao
Keyword(s):  
Feature Fusion ◽  
Binary Classification ◽  
Classification Problem ◽  
Detection Algorithm ◽  
Video Sequences ◽  
Image Block ◽  
Body Contour ◽  
Adaboost Algorithm ◽  
Target Model ◽  
Detection And Tracking

In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.

Circular hole detection algorithm based on image block

2018 ◽  
Vol 78 (21) ◽  
pp. 29659-29679
Author(s):  
Lianyuan Jiang ◽  
Haohao Yuan ◽  
Chungui Li
Keyword(s):  
Circular Hole ◽  
Detection Algorithm ◽  
Image Block ◽  
Hole Detection

Improved Algorithm for Sub-Pixel Edge Detection Based on Zernike Moments

2011 ◽  
Vol 341-342 ◽  
pp. 763-767
Author(s):  
Bao Yong Zhao ◽  
Ying Jian Qi
Keyword(s):  
Edge Detection ◽  
Detection Algorithm ◽  
Zernike Moments ◽  
The Other ◽  
Original Image ◽  
Edge Detection Algorithm ◽  
Series Of Experiments ◽  
The One ◽  
New Criterion ◽  
Improved Algorithm

The principle of Zernike moments and the method of sub-pixel edge detection based on Zernike moments were introduced in this paper. With the consideration of the limitation of the sub-pixel edge detection algorithm by Ghosal, such as the lower location precision of the edge and the extracted wider edge than that of the original image, an improved algorithm was proposed. On the one hand, a mask of size nine multiply nine was calculated and could be applied for the edge detection. On the other hand, a new criterion for edge detection was put forward. Additionally, a series of experiments were designed and implemented. The experiment results show that accuracy of the improved algorithm is higher than that obtained from using other size templates and Ghosal algorithm.

A Rapid Body Limb Detection Algorithm for Video Sequence Image

2012 ◽  
Vol 433-440 ◽  
pp. 4324-4329
Author(s):  
Hua Chen ◽  
Chun Hai Hu ◽  
Shu Tao Wang
Keyword(s):  
Edge Detection ◽  
Efficient Algorithm ◽  
Background Noise ◽  
Video Sequence ◽  
Detection Algorithm ◽  
The Body ◽  
Original Video ◽  
Original Image ◽  
Integral Image ◽  
Pixel Value

We describe an efficient algorithm to detect the limbs of a human. In order to realize the real-time and robustness, we utilize a detection algorithm by using integral image and haar-like feature. The integral image is the original image for video, the pixel value of each point is the point of the original image in the top left of all the pixel values. After the convolution of the edge detection template and the original video for each frame, convolution images obtained information and suppress background noise, the body physical location can be calculated by accumulating the image of the integral image and by using haar-like feature. The experiment results show the algorithm can detect the location of the limbs at the rate of 30 frames per second.

scholarly journals Nonreference Medical Image Edge Map Measure

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Karen Panetta ◽  
Chen Gao ◽  
Sos Agaian ◽  
Shahan Nercessian
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Medical Image ◽  
Visual Analysis ◽  
Medical Image Processing ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Reconstructed Image ◽  
False Alarms ◽  
Original Image

Edge detection is a key step in medical image processing. It is widely used to extract features, perform segmentation, and further assist in diagnosis. A poor quality edge map can result in false alarms and misses in cancer detection algorithms. Therefore, it is necessary to have a reliable edge measure to assist in selecting the optimal edge map. Existing reference based edge measures require a ground truth edge map to evaluate the similarity between the generated edge map and the ground truth. However, the ground truth images are not available for medical images. Therefore, a nonreference edge measure is ideal for medical image processing applications. In this paper, a nonreference reconstruction based edge map evaluation (NREM) is proposed. The theoretical basis is that a good edge map keeps the structure and details of the original image thus would yield a good reconstructed image. The NREM is based on comparing the similarity between the reconstructed image with the original image using this concept. The edge measure is used for selecting the optimal edge detection algorithm and optimal parameters for the algorithm. Experimental results show that the quantitative evaluations given by the edge measure have good correlations with human visual analysis.

A New Fragile Watermarking Based on Chaos

2013 ◽  
Vol 401-403 ◽  
pp. 1772-1775
Author(s):  
Hui Fen Huang
Keyword(s):  
Good Practice ◽  
Chaotic Sequence ◽  
Fragile Watermarking ◽  
Image Feature ◽  
Original Image ◽  
Content Authentication ◽  
Tamper Localization ◽  
Image Block ◽  
Positioning Accuracy ◽  
Security Issues

For existing airspace fragile authentication watermarking algorithm positioning accuracy and security issues, this paper presents a fully-fragile watermarking based on chaos for the integrity of the image content authentication and tamper localization. The algorithm uses the original image block, the calculation of each pixel in the image block high-bit gray value as the image feature watermark information. The chaotic sequence is encrypted and determines the position of the watermark bit is embedded watermark information, tampering with the positioning accuracy of an image block of 2 × 2 pixels. Experimental results show that the algorithm is simple, safe, with good practice.

Multi-Focus Fusion Based on the Imaging Fuzzy Mechanism

2012 ◽  
Vol 239-240 ◽  
pp. 135-139
Author(s):  
Xiao Feng Yue ◽  
Tao Shen Li ◽  
Zi Xin Feng
Keyword(s):  
Image Fusion ◽  
Wavelet Decomposition ◽  
Evaluation Criteria ◽  
Original Image ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Image Block ◽  
Time Performance ◽  
Block Form ◽  
Imaging Theory

This paper put forward an image fusion algorithm which based on the analysis of the imaging theory fusion, the fusion process is first will have been registration of the original image into several pieces, we calculate the correlation of corresponding piece, as the image contrast evaluation criteria, by selecting two images in the clear image block form fusion image. This paper discusses the principle analysis from optical imaging, the cause of the fusion algorithm, to avoid the use of Gaussian Blur model to explain the image of a fuzzy controversy. The experimental results show that the proposed algorithm is good real-time performance, for registration fusion image can achieve even more than general wavelet decomposition of the fusion algorithm.

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