Improving the experimental analysis of tampered image detection algorithms for biometric systems

2018 ◽  
Vol 113 ◽  
pp. 93-101
Author(s):  
Giuseppe Cattaneo ◽  
Gianluca Roscigno ◽  
Umberto Ferraro Petrillo
Keyword(s):  
Experimental Analysis ◽  
Image Detection ◽  
Detection Algorithms ◽  
Biometric Systems ◽  
Tampered Image

scholarly journals Image target detection algorithm compression and pruning based on neural network

2021 ◽  
Vol 18 (2) ◽  
pp. 499-516
Author(s):  
Yan Sun ◽  
Zheping Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Network Model ◽  
Detection Algorithm ◽  
Image Feature ◽  
Propagation Mode ◽  
Learning Technology ◽  
Image Detection ◽  
Detection Algorithms

The main purpose of target detection is to identify and locate targets from still images or video sequences. It is one of the key tasks in the field of computer vision. With the continuous breakthrough of deep machine learning technology, especially the convolutional neural network model shows strong Ability to extract image feature in the field of digital image processing. Although the model research of target detection based on convolutional neural network is developing rapidly, but there are still some problems in practical applications. For example, a large number of parameters requires high storage and computational costs in detected model. Therefore, this paper optimizes and compresses some algorithms by using early image detection algorithms and image detection algorithms based on convolutional neural networks. After training and learning, there will appear forward propagation mode in the application of CNN network model, providing the model for image feature extraction, integration processing and feature mapping. The use of back propagation makes the CNN network model have the ability to optimize learning and compressed algorithm. Then research discuss the Faster-RCNN algorithm and the YOLO algorithm. Aiming at the problem of the candidate frame is not significant which extracted in the Faster- RCNN algorithm, a target detection model based on the Significant area recommendation network is proposed. The weight of the feature map is calculated by the model, which enhances the saliency of the feature and reduces the background interference. Experiments show that the image detection algorithm based on compressed neural network image has certain feasibility.

An Experimental Analysis of Community Detection Algorithms on a Temporally Evolving Dataset

2021 ◽  
pp. 23-70
Author(s):  
B.S.A.S. Rajita ◽  
Mrinalini Shukla ◽  
Deepa Kumari ◽  
Subhrakanta Panda
Keyword(s):  
Community Detection ◽  
Experimental Analysis ◽  
Detection Algorithms

scholarly journals A Detection Method of Operated Fake-Images Using Robust Hashing

2021 ◽  
Vol 7 (8) ◽  
pp. 134
Author(s):  
Miki Tanaka ◽  
Sayaka Shiota ◽  
Hitoshi Kiya
Keyword(s):  
Reference Image ◽  
Query Image ◽  
Image Detection ◽  
Practical Applications ◽  
Hash Code ◽  
Novel Method ◽  
Conventional Methods ◽  
Tampered Image ◽  
First Time ◽  
A Company

SNS providers are known to carry out the recompression and resizing of uploaded images, but most conventional methods for detecting fake images/tampered images are not robust enough against such operations. In this paper, we propose a novel method for detecting fake images, including distortion caused by image operations such as image compression and resizing. We select a robust hashing method, which retrieves images similar to a query image, for fake-image/tampered-image detection, and hash values extracted from both reference and query images are used to robustly detect fake-images for the first time. If there is an original hash code from a reference image for comparison, the proposed method can more robustly detect fake images than conventional methods. One of the practical applications of this method is to monitor images, including synthetic ones sold by a company. In experiments, the proposed fake-image detection is demonstrated to outperform state-of-the-art methods under the use of various datasets including fake images generated with GANs.

scholarly journals Comparative Analysis of Deep Learning Image Detection Algorithms

2020 ◽  
Author(s):  
Shrey Srivast ◽  
Amit Vishvas Divekar ◽  
Chandu Anilkumar ◽  
Ishika Naik ◽  
Ved Kulkarni ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Visual Media ◽  
Image Detection ◽  
Detection Algorithms ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
The Moment

Abstract As humans, we do not have to strain ourselves when we interpret our surroundings through our visual senses. From the moment we begin to observe, we unconsciously train ourselves with the same set of images. Hence, distinguishing entities is not a difficult task for us. On the contrary, computer views all kinds of visual media as an array of numerical values. Due to this contrast in approach, they require image processing algorithms to examine the contents of images. This project presents a comparative analysis of 3 major image processing algorithms: SSD, Faster R-CNN, and YOLO. In this analysis, we have chosen the COCO dataset. With the help of the COCO dataset, we have evaluated the performance and accuracy of the three algorithms and analysed their strengths and weaknesses. Using the results obtained from our implementations, we determine the differences between how each algorithm runs and suitable applications for each. The parameters for evaluation are accuracy, precision, F1 score.

scholarly journals Development and Application of Fire Video Image Detection Technology in China’s Road Tunnels

2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Wentao Dai ◽  
Jixun Jiang ◽  
Guofeng Ding ◽  
Zhigang Liu
Keyword(s):  
Fire Safety ◽  
Fire Detection ◽  
Video Image ◽  
Detection Capability ◽  
Image Detection ◽  
Urban Road ◽  
Detection Algorithms ◽  
Detection Technology ◽  
Road Tunnels ◽  
Detection Technologies

A large number of highway tunnels, urban road tunnels and underwater tunnels have been constructed throughout China over the last two decades. With the rapid increase in vehicle traffic, the number of fire incidents in road tunnels have also substantially increased. This paper aims to review the development and application of fire video image detection (VID) technology and their impact on fire safety in China’s road tunnels. The challenges of fire safety in China’s road tunnels are analyzed. The capabilities and limitations of fire detection technologies currently used in China’s road tunnels are discussed. The research and development of fire VID technology in road tunnels, including various detection algorithms, evolution of VID systems and evaluation of their performances in various tunnel tests are reviewed. Some cases involving VID applications in China’s road tunnels are reported. The studies show that the fire VID systems have unique features in providing fire protection and their detection capability and reliability have been enhanced over the decades with the advance in detection algorithms, hardware and integration with other tunnel systems. They have become an important safety system in China’s road tunnels.

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