Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

2020 ◽  
pp. 1379-1394
Author(s):  
Lu Liu ◽  
Yao Zhao ◽  
Rongrong Ni ◽  
Qi Tian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Color Filter ◽  
Post Processing ◽  
Filter Array ◽  
Cfa Interpolation ◽  
Learned Features

This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately localize the tampered regions, which combines color filter array (CFA) features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.

Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

2018 ◽  
Vol 10 (4) ◽  
pp. 140-155 ◽  
Author(s):  
Lu Liu ◽  
Yao Zhao ◽  
Rongrong Ni ◽  
Qi Tian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Color Filter ◽  
Interpolation Algorithm ◽  
Post Processing ◽  
Classification Result ◽  
Filter Array ◽  
Cfa Interpolation ◽  
Learned Features

This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately localize the tampered regions, which combines color filter array (CFA) features. The CFA interpolation algorithm introduces the correlation and consistency among the pixels, which can be easily destroyed by most image processing operations. The proposed CNN method can effectively distinguish the traces caused by copy-move forgeries and some post-processing operations. Additionally, it can utilize the classification result to guide the feature extraction, which can enhance the robustness of the learned features. This article, per the authors, tests the proposed method in several experiments. The results demonstrate the efficiency of the method on different forgeries and quantifies its robustness and sensitivity.

scholarly journals The Role and Utilization of CNN in Automatic Logo Based Document Image Retrieval Methods

2018 ◽  
Vol 7 (3.1) ◽  
pp. 13
Author(s):  
Raveendra K ◽  
R Vinoth Kanna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Document Image ◽  
Retrieval Process ◽  
Pictorial Representations

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.  

scholarly journals Automatic Detection of Modulation Scheme Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Blessy Babu ◽  
Hari V Sreeniva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
System Performance ◽  
Automatic Detection ◽  
Training Dataset ◽  
Modulation Scheme ◽  
Intelligent Detection

Abstract This paper summarizes the intelligent detection of modulation scheme in an incoming signal, build on convolutional neural network (CNN). It describes the creation of training dataset, realization of CNN, testing and validation. The raw modulated signals are converted into 2D and put on to the network for training. The resulting prototype is adopted for detection. The results signify that the intended approach gives better prediction for the identification of modulated signal without need for any selective feature extraction. The system performance on noise is also evaluated and modelled.

scholarly journals Progressive Cascaded Convolutional Neural Networks for Single Tree Detection with Google Earth Imagery

2019 ◽  
Vol 11 (15) ◽  
pp. 1786 ◽  
Author(s):  
Tianyang Dong ◽  
Yuqi Shen ◽  
Jian Zhang ◽  
Yang Ye ◽  
Jing Fan
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Google Earth ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Tree Detection ◽  
Single Tree

High-resolution remote sensing images can not only help forestry administrative departments achieve high-precision forest resource surveys, wood yield estimations and forest mapping but also provide decision-making support for urban greening projects. Many scholars have studied ways to detect single trees from remote sensing images and proposed many detection methods. However, the existing single tree detection methods have many errors of commission and omission in complex scenes, close values on the digital data of the image for background and trees, unclear canopy contour and abnormal shape caused by illumination shadows. To solve these problems, this paper presents progressive cascaded convolutional neural networks for single tree detection with Google Earth imagery and adopts three progressive classification branches to train and detect tree samples with different classification difficulties. In this method, the feature extraction modules of three CNN networks are progressively cascaded, and the network layer in the branches determined whether to filter the samples and feed back to the feature extraction module to improve the precision of single tree detection. In addition, the mechanism of two-phase training is used to improve the efficiency of model training. To verify the validity and practicability of our method, three forest plots located in Hangzhou City, China, Phang Nga Province, Thailand and Florida, USA were selected as test areas, and the tree detection results of different methods, including the region-growing, template-matching, convolutional neural network and our progressive cascaded convolutional neural network, are presented. The results indicate that our method has the best detection performance. Our method not only has higher precision and recall but also has good robustness to forest scenes with different complexity levels. The F1 measure analysis in the three plots was 81.0%, which is improved by 14.5%, 18.9% and 5.0%, respectively, compared with other existing methods.

scholarly journals Intellectual Acute Lymphoblastic Leukemia (ALL) Detection Model for Diagnosis of Blood Cancer from microscopic images using Hybrid Convolutional Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 2972-2981
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
Blood Cancer ◽  
Microscopic Images ◽  
Feature Extraction And Selection ◽  
Evaluation Parameters ◽  
State Of Art

Blood cell malignantly growth has been accounted for to be one of the most transcendent types of disease maladies. ALL (Acute Lymphoblastic Leukaemias) is the malignant types of blood cancer and their detection and classification in earlier stage is biggest issue. Automatic detection and classification of ALL from microscopic images is a challenging and intellectual assignment in medical science. Existing techniques for ALL detection and classification are an understandable alternative for real-time dermoscopic data analysis. Existing microscopic image processing approaches are unable to analyze the ALL data with non-stationary nature. In this perspective, the focus of this research is to design hybrid Convolutional Neural Network (CNN) architecture by utilizing Firefly Optimization Algorithm (FOA/FFA) to detect the ALL from microscopic images of human blood cell into malignant or normal blood cell. Methods: For training and testing of proposed ALL Detection and Classification (ALL-DC) Model, Standard ALL-IDB (Acute-Lymphoblastic-Leukaemias Image Database for Image Processing) is used with hybrid CNN architecture based on the FOA. Here, Histogram of Oriented Gradients (HOG) descriptor with FOA is used as feature extraction and selection mechanism from the Region of Blood Cell (ROBC).Feature extraction approach plays an important responsibility to classify lots of blood diseases. On the way to achieve this goal, we proposed ALL-DC model that combines recent developments in deep learning with fuzzy based CNN structure and for ROBC segmentation, hybridization of K-means segmentation algorithm with FOA that are capable to segment the accurate blood cell region from microscopic images. Using k-means segmentation technique, the foreground and background component is separated into two regions and after that to improve the segmentation results; FOA is used with the novel concept of image enhancement approach. Results: The proposed ALL-DC system is evaluated using the largest publicly accessible standard ALL-IDB dataset, containing 600 training and 400 testing microscopic images. When the evaluation parameters of proposed work is compared with a number of other state-of-art schemes, the proposed scheme achieves the most excellent performance of 98.5% in terms of accuracy which also known as area under the curve (AUC) in differentiating ALL from benign cell using only the extracted and optimized HOG feature. Conclusion: When the proposed model is tested on different microscopic images, evaluation parameters is calculated and compared with a few other state-of-art methods and we obtained the proposed method achieves the best performance in terms of classification accuracy. ALL-DC model is implemented and constructed using the concept of Image

scholarly journals A Novel Robust Classification Method for Ground-Based Clouds

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 999
Author(s):  
Aihua Yu ◽  
Ming Tang ◽  
Gang Li ◽  
Beiping Hou ◽  
Zhongwei Xuan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Sparse Representation ◽  
Convolutional Neural Networks ◽  
Recognition Rate ◽  
Classification Method ◽  
Robust Classification ◽  
Cloud Classification

Though the traditional convolutional neural network has a high recognition rate in cloud classification, it has poor robustness in cloud classification with occlusion. In this paper, we propose a novel scheme for cloud classification, in which the convolutional neural networks are used for feature extraction and a weighted sparse representation coding is adopted for classification. Three such algorithms are proposed. Experiments are carried out using the multimodal ground-based cloud dataset and the results show that in the case of occlusion, the accuracy of the proposed methods can be much improved over the traditional convolutional neural network-based algorithms.

scholarly journals Superresolution Reconstruction of Remote Sensing Image Based on Middle-Level Supervised Convolutional Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Xiu Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Feature Extraction ◽  
Image Reconstruction ◽  
Convolutional Neural Network ◽  
Middle Layer ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Extraction Stage

Image has become one of the important carriers of visual information because of its large amount of information, easy to spread and store, and strong sense of sense. At the same time, the quality of image is also related to the completeness and accuracy of information transmission. This research mainly discusses the superresolution reconstruction of remote sensing images based on the middle layer supervised convolutional neural network. This paper designs a convolutional neural network with middle layer supervision. There are 16 layers in total, and the seventh layer is designed as an intermediate supervision layer. At present, there are many researches on traditional superresolution reconstruction algorithms and convolutional neural networks, but there are few researches that combine the two together. Convolutional neural network can obtain the high-frequency features of the image and strengthen the detailed information; so, it is necessary to study its application in image reconstruction. This article will separately describe the current research status of image superresolution reconstruction and convolutional neural networks. The middle supervision layer defines the error function of the supervision layer, which is used to optimize the error back propagation mechanism of the convolutional neural network to improve the disappearance of the gradient of the deep convolutional neural network. The algorithm training is mainly divided into four stages: the original remote sensing image preprocessing, the remote sensing image temporal feature extraction stage, the remote sensing image spatial feature extraction stage, and the remote sensing image reconstruction output layer. The last layer of the network draws on the single-frame remote sensing image SRCNN algorithm. The output layer overlaps and adds the remote sensing images of the previous layer, averages the overlapped blocks, eliminates the block effect, and finally obtains high-resolution remote sensing images, which is also equivalent to filter operation. In order to allow users to compare the superresolution effect of remote sensing images more clearly, this paper uses the Qt5 interface library to implement the user interface of the remote sensing image superresolution software platform and uses the intermediate layer convolutional neural network and the remote sensing image superresolution reconstruction algorithm proposed in this paper. When the training epoch reaches 35 times, the network has converged. At this time, the loss function converges to 0.017, and the cumulative time is about 8 hours. This research helps to improve the visual effects of remote sensing images.

scholarly journals Forest fire image recognition based on convolutional neural network

2019 ◽  
Vol 13 ◽  
pp. 174830261988768 ◽  
Author(s):  
Yuanbin Wang ◽  
Langfei Dang ◽  
Jieying Ren
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Forest Fire ◽  
Recognition Rate ◽  
Extraction Process ◽  
Fire Flame

In order to detect fire automatically, a forest fire image recognition method based on convolutional neural networks is proposed in this paper. There are two main types of fire recognition algorithms. One is based on traditional image processing technology and the other is based on convolutional neural network technology. The former is easy to lead in false detection because of blindness and randomness in the stage of feature selection, while for the latter the unprocessed convolutional neural network is applied directly, so that the characteristics learned by the network are not accurate enough, and recognition rate may be affected. In view of these problems, conventional image processing techniques and convolutional neural networks are combined, and an adaptive pooling approach is introduced. The fire flame area can be segmented and the characteristics can be learned by this algorithm ahead. At the same time, the blindness in the traditional feature extraction process is avoided, and the learning of invalid features in the convolutional neural network is also avoided. Experiments show that the convolutional neural network method based on adaptive pooling method has better performance and has higher recognition rate.

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