Distinguishing Computer Graphics from Natural Images Based on Statistical Characteristics

2013 ◽  
Vol 380-384 ◽  
pp. 1306-1309
Author(s):  
Sen Feng Tong ◽  
Yu Hao Yang ◽  
Yong Jie Xie
Keyword(s):  
Computer Graphics ◽  
Discrete Cosine Transform ◽  
Recognition Rate ◽  
Experimental Results ◽  
Natural Images ◽  
Statistical Characteristics ◽  
Transform Domain ◽  
Significant Digit ◽  
Detection Model ◽  
Discrimination Method

In this paper, we propose a new discrimination method using image statistical characteristics is proposed which is designed to distinguish natural images from photorealistic computer graphics. Using Benford model as statistical basis, we conclude statistical properties of the MSD (most significant digit) of AC (Alternating Current) coefficients in DCT (Discrete Cosine Transform) domain of natural images and computer graphics, and then we constructed the detection model of the proposed algorithm. Experimental results show that this method can identify natural images and computer graphics effectively, compared with the existing algorithms this method has a higher recognition rate, which comes to 95.22%.

A Steganalysis Method for 2D Engineering Graphics Based on the Statistic of Geometric Features

2011 ◽  
Vol 3 (2) ◽  
pp. 35-40
Author(s):  
Fei Peng ◽  
Honglin Li
Keyword(s):  
Experimental Results ◽  
Statistical Characteristics ◽  
Geometric Features ◽  
Hidden Information ◽  
Engineering Graphics ◽  
Detection Model ◽  
Straight Line ◽  
Steganalysis Method

Aiming at hiding information in 2D engineering graphics based on geometric features, a steganalysis method is proposed in this paper. First, the authors obtained the number of 2D engineering graphics’ strictly vertical and horizontal lines and identify the number of horizontal and vertical lines which are deviated from the straight line within a certain range. Subsequently, the authors selected the ratio between the deviated lines and the normal lines as the statistical characteristics. Finally, a detection model was constructed based on the hypothesis. Experimental results show that the algorithm can detect hidden information in the 2D engineering graphics effectively.

scholarly journals Block-Matching Based Multifocus Image Fusion

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Feng Zhu ◽  
Yingkun Hou ◽  
Jingyu Yang
Keyword(s):  
Image Fusion ◽  
Discrete Cosine Transform ◽  
Weighted Average ◽  
Objective Evaluation ◽  
Block Matching ◽  
Experimental Results ◽  
Transform Domain ◽  
Fusion Method ◽  
Spatial Methods ◽  
Image Fusion Method

A new multifocus image fusion method is proposed. Two image blocks are selected by sliding the window from the two source images at the same position, discrete cosine transform (DCT) is implemented, respectively, on these two blocks, and the alternating component (AC) energy of these blocks is then calculated to decide which is the well-focused one. In addition, block matching is used to determine a group of image blocks that are all similar to the well-focused reference block. Finally, all the blocks are returned to their original positions through weighted average. The weight is decided with the AC energy of the well-focused block. Experimental results demonstrate that, unlike other spatial methods, the proposed method effectively avoids block artifacts. The proposed method also significantly improves the objective evaluation results, which are obtained by some transform domain methods.

scholarly journals SwinGD: A Robust Grape Bunch Detection Model Based on Swin Transformer in Complex Vineyard Environment

Horticulturae ◽  
2021 ◽  
Vol 7 (11) ◽  
pp. 492
Author(s):  
Jinhai Wang ◽  
Zongyin Zhang ◽  
Lufeng Luo ◽  
Wenbo Zhu ◽  
Jianwen Chen ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Recognition Rate ◽  
Image Data ◽  
Optimal Number ◽  
Experimental Results ◽  
Good Effect ◽  
Practical Application ◽  
Detection Model ◽  
Red Grape ◽  
Detection Effect

Accurate recognition of fruits in the orchard is an important step for robot picking in the natural environment, since many CNN models have a low recognition rate when dealing with irregularly shaped and very dense fruits, such as a grape bunch. It is a new trend to use a transformer structure and apply it to a computer vision domain for image processing. This paper provides Swin Transformer and DETR models to achieve grape bunch detection. Additionally, they are compared with traditional CNN models, such as Faster-RCNN, SSD, and YOLO. In addition, the optimal number of stages for a Swin Transformer through experiments is selected. Furthermore, the latest YOLOX model is also used to make a comparison with the Swin Transformer, and the experimental results show that YOLOX has higher accuracy and better detection effect. The above models are trained under red grape datasets collected under natural light. In addition, the dataset is expanded through image data augmentation to achieve a better training effect. After 200 epochs of training, SwinGD obtained an exciting mAP value of 94% when IoU = 0.5. In case of overexposure, overdarkness, and occlusion, SwinGD can recognize more accurately and robustly compared with other models. At the same time, SwinGD still has a better effect when dealing with dense grape bunches. Furthermore, 100 pictures of grapes containing 655 grape bunches are downloaded from Baidu pictures to detect the effect. The Swin Transformer has an accuracy of 91.5%. In order to verify the universality of SwinGD, we conducted a test under green grape images. The experimental results show that SwinGD has a good effect in practical application. The success of SwinGD provides a new solution for precision harvesting in agriculture.

scholarly journals Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Shan Wang ◽  
Sulaiman Khan ◽  
Chuyi Xu ◽  
Shah Nazir ◽  
Abdul Hafeez
Keyword(s):  
Random Forest ◽  
Model Development ◽  
Recognition Rate ◽  
Real Life ◽  
Experimental Results ◽  
Detection Model ◽  
Heterogeneous Devices ◽  
Proposed Model ◽  
Communication Needs ◽  
Phishing Detection

With the increase in the number of electronic devices and developments in the communication system, security becomes one of the challenging issues. Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. Such communication needs a secure medium through which users can communicate in a secure and reliable way so that their information may not be lost. The proposed study is an endeavor toward the detection of phishing by using random forest and BLSTM classifiers. The experimental results of the proposed study are promising in phishing detection, and the study reflects the applicability of the proposed algorithms in the information security. The experimental results show that the BLSTM-based phishing detection model is prominent in ensuring the network security by generating a recognition rate of 95.47% compared to the conventional RF-based model that generates a recognition rate of 87.53%. This high recognition rate for the BLSTM-based model reflects the applicability of the proposed model for phishing detection.

Construction and Reduction Methods of Web Spam Identification Index System

2019 ◽  
Vol 12 (3) ◽  
pp. 202-211
Author(s):  
Yuancheng Li ◽  
Rong Huang ◽  
Xiangqian Nie
Keyword(s):  
Index System ◽  
Rapid Development ◽  
Recognition Rate ◽  
Computing Power ◽  
Detection Model ◽  
Web Spam ◽  
Reduction Methods ◽  
Massive Scale ◽  
The Index System ◽  
The Web

Background: With the rapid development of the Internet, the number of web spam has increased dramatically in recent years, which has wasted search engine storage and computing power on a massive scale. To identify the web spam effectively, the content features, link features, hidden features and quality features of web page are integrated to establish the corresponding web spam identification index system. However, the index system is highly correlation dimension. Methods: An improved method of autoencoder named stacked autoencoder neural network (SAE) is used to realize the reduction of the web spam identification index system. Results: The experiment results show that our method could reduce effectively the index of web spam and significantly improves the recognition rate in the following work. Conclusion: An autoencoder based web spam indexes reduction method is proposed in this paper. The experimental results show that it greatly reduces the temporal and spatial complexity of the future web spam detection model.

Aircraft Cable Fault Location Based on Detection Model

2011 ◽  
Vol 55-57 ◽  
pp. 332-336 ◽  
Author(s):  
Xiao Lin Liu ◽  
Zhi Quan Li
Keyword(s):  
Fault Location ◽  
Reference Signal ◽  
Experimental Results ◽  
Civil Aviation ◽  
Location Method ◽  
Aviation Maintenance ◽  
Location System ◽  
Detection Model ◽  
Experimental Installation

An aircraft cable fault location method based on detection model is proposed to solve the problem of being difficult to inspect the fault for the civil aviation maintenance. In response to the condition of the experimental installation, the reference signal is designed. The fault of the cable can be located according to the reflected waveform. An aircraft cable fault location system is designed and the experimental results show that the method is rational and effective.

Recognition of 3D Objects from 2D Views Features

2015 ◽  
Vol 13 (2) ◽  
pp. 50-58
Author(s):  
R. Khadim ◽  
R. El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Color Image ◽  
Recognition Rate ◽  
Experimental Results ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
3D Objects ◽  
Color Descriptor

This paper focuses on the recognition of 3D objects using 2D attributes. In order to increase the recognition rate, the present an hybridization of three approaches to calculate the attributes of color image, this hybridization based on the combination of Zernike moments, Gist descriptors and color descriptor (statistical moments). In the classification phase, three methods are adopted: Neural Network (NN), Support Vector Machine (SVM), and k-nearest neighbor (KNN). The database COIL-100 is used in the experimental results.

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