scholarly journals Deepfake Detection Method Based on Cross-Domain Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fang Sun ◽  
Niuniu Zhang ◽  
Pan Xu ◽  
Zengren Song
Keyword(s):  
Classification Accuracy ◽  
Detection Method ◽  
Image Features ◽  
Security Threats ◽  
Geometric Features ◽  
Fake News ◽  
Domain Fusion ◽  
Cross Domain ◽  
Facial Information ◽  
Active Detection

In recent years, despite its wide use in various fields, deepfake has been abused to generate hazardous contents such as fake movies, rumors, and fake news by manipulating or replacing facial information of the original sources and, thus, exerts huge security threats to the society. Facing the continuous evolution of deepfake, research on active detection and prevention technology becomes particularly important. In this paper, we propose a new deepfake detection method based on cross-domain fusion, which, on the basis of traditional spatial domain features, realizes the fusion of cross-domain image features by introducing edge geometric features of the frequency domain and, therefore, achieves considerable improvements on classification accuracy. Further evaluations of this method have been performed on publicly deepfake datasets, and the results show that our method is effective particularly on the Meso-4 DeepFake Database.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

scholarly journals Multilayer Hybrid Deep-Learning Method for Waste Classification and Recycling

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yinghao Chu ◽  
Chen Huang ◽  
Xiaodan Xie ◽  
Bohai Tan ◽  
Shyam Kamal ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Classification Accuracy ◽  
Image Features ◽  
Learning System ◽  
Multilayer Perceptrons ◽  
Learning Method ◽  
Waste Classification ◽  
Feature Information ◽  
Public Area

This study proposes a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high-resolution camera to capture waste image and sensors to detect other useful feature information. The MHS uses a CNN-based algorithm to extract image features and a multilayer perceptrons (MLP) method to consolidate image features and other feature information to classify wastes as recyclable or the others. The MHS is trained and validated against the manually labelled items, achieving overall classification accuracy higher than 90% under two different testing scenarios, which significantly outperforms a reference CNN-based method relying on image-only inputs.

scholarly journals Source Camera Identification Issues

2011 ◽  
Vol 3 (4) ◽  
pp. 1-15
Author(s):  
Yongjian Hu ◽  
Chang-Tsun Li ◽  
Changhui Zhou ◽  
Xufeng Lin
Keyword(s):  
Classification Accuracy ◽  
Selection Criteria ◽  
Current Source ◽  
Image Features ◽  
Forensic Identification ◽  
Forensic Investigation ◽  
Identification Schemes ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Popular Image

Statistical image features play an important role in forensic identification. Current source camera identification schemes select image features mainly based on classification accuracy and computational efficiency. For forensic investigation purposes; however, these selection criteria are not enough. Consider most real-world photos may have undergone common image processing due to various reasons, source camera classifiers must have the capability to deal with those processed photos. In this work, the authors first build a sample camera classifier using a combination of popular image features, and then reveal its deficiency. Based on the experiments, suggestions for the design of robust camera classifiers are given.

Laser Printing Files Detection Method Based on Double Features

2018 ◽  
Vol 32 (10) ◽  
pp. 1854028
Author(s):  
Juan Zhu ◽  
Jipeng Huang ◽  
Lianming Wang
Keyword(s):  
Detection Method ◽  
Texture Features ◽  
Image Features ◽  
Scale Invariant ◽  
Laser Printing ◽  
Valid Data ◽  
Sift Features ◽  
Low Efficiency ◽  
Original File ◽  
Histogram Feature

A novel laser printing files detection method is proposed in this paper to solve the problem of low efficiency and difficulty in traditional detection. The new method is based on improved scale-invariant feature transform (SIFT) feature and histogram feature. Firstly, analyze the graphical features of different laser printing files. Different files have different printing texture features in valid data area. So segment the valid data area to remove the interference of background. Secondly, extract the histogram feature of the same character in the printing file. Normalize the histogram and then calculate the Bhattacharyya coefficient between the detected file and the original file to determine whether the detected file is right or fake. At the same time, calculate the SIFT features and match the detected file and the original file. To focus on the letter or character region, the SIFT features which are out of contour are deleted. Finally, the results of the two different methods are both used as the result of the identification. When any of the result is fake, the end result will be fake. In the self-built database experiment, in different printing files from different printers, the inkjet areas possess different image features. When scanning different files using 600 dpi, the detect accuracy is higher than 97%. This method was able to meet the reliability requirements of law.

A Transformer-Based Approach to Multilingual Fake News Detection in Low-Resource Languages

2022 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
Arkadipta De ◽  
Dibyanayan Bandyopadhyay ◽  
Baban Gain ◽  
Asif Ekbal
Keyword(s):  
Language Processing ◽  
English Language ◽  
Neural Model ◽  
Fake News ◽  
Web Content ◽  
Low Resource ◽  
Domain Specific ◽  
Cross Domain ◽  
Current State ◽  
Domain Transfer

Fake news classification is one of the most interesting problems that has attracted huge attention to the researchers of artificial intelligence, natural language processing, and machine learning (ML). Most of the current works on fake news detection are in the English language, and hence this has limited its widespread usability, especially outside the English literate population. Although there has been a growth in multilingual web content, fake news classification in low-resource languages is still a challenge due to the non-availability of an annotated corpus and tools. This article proposes an effective neural model based on the multilingual Bidirectional Encoder Representations from Transformer (BERT) for domain-agnostic multilingual fake news classification. Large varieties of experiments, including language-specific and domain-specific settings, are conducted. The proposed model achieves high accuracy in domain-specific and domain-agnostic experiments, and it also outperforms the current state-of-the-art models. We perform experiments on zero-shot settings to assess the effectiveness of language-agnostic feature transfer across different languages, showing encouraging results. Cross-domain transfer experiments are also performed to assess language-independent feature transfer of the model. We also offer a multilingual multidomain fake news detection dataset of five languages and seven different domains that could be useful for the research and development in resource-scarce scenarios.

Research on the Application of New Active Inverter Technology in Battery Maintenance

2013 ◽  
Vol 448-453 ◽  
pp. 2077-2084
Author(s):  
Bing Yu Li ◽  
Ke Xu ◽  
Yun Ming Li ◽  
Xuan Yi Li
Keyword(s):  
Pulse Width Modulation ◽  
Detection Method ◽  
Power Station ◽  
Inspection Method ◽  
Discharge System ◽  
Passive Detection ◽  
Three Phase ◽  
Active Detection ◽  
And Control ◽  
Control Topology

This paper presents a discharge inspection method based on new active inverter feedback discharge after comparison with the check discharge from pure resistance of power station batteries. A new active inverter system has been developed with SPWM pulse width modulation technology, which is based on DC/DC circuitry, along with a three-phase full bridge inverter and control topology circuits. In addition, the principle for selecting the parameters of RCL elements of the system has been analyzed. This paper also presents a detection method combining active detection with passive detection for the islanding effect which exists in the grid-connected discharge system, according to the islanding effect of grid-connected discharge equipment, so as to stably discharge without interference on the grid.

A Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.

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