scholarly journals Multi-Layer Fusion Neural Network for Deepfake Detection

2021 ◽  
Vol 13 (4) ◽  
pp. 26-39
Author(s):  
Zheng Zhao ◽  
Penghui Wang ◽  
Wei Lu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Experimental Results ◽  
Detection Methods ◽  
Semantic Features ◽  
Statistical Features ◽  
New Approach ◽  
Deep Layers ◽  
Different Levels

Recently, the spread of videos forged by deepfake tools has been widely concerning, and effective ways for detecting them are urgently needed. It is known that such artificial intelligence-aided forgery makes at least three levels of artifacts, which can be named as microcosmic or statistical features, mesoscopic features, and macroscopic or semantic features. However, existing detection methods have not been designed to exploited them all. This work proposes a new approach to more effective detection of deepfake videos. A multi-layer fusion neural network (MFNN) has been designed to capture the artifacts in different levels. Features maps output from specially designed shallow, middle, and deep layers, which are used as statistical, mesoscopic, and semantic features, respectively, are fused together before classification. FaceForensic++ dataset was used to train and test the method. The experimental results show that MFNN outperforms other relevant methods. Particularly, it demonstrates more advantage in detecting low-quality deepfake videos.

scholarly journals Traffic marking recognition based on generating antagonistic neural network

2019 ◽  
Vol 136 ◽  
pp. 04076 ◽  
Author(s):  
Shuwei Xu ◽  
Shan Zhang ◽  
Shuwei Xu
Keyword(s):  
Neural Network ◽  
Markov Chain ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Methods ◽  
Image Detection ◽  
Fast Recognition ◽  
Repeated Sampling ◽  
Backward Propagation ◽  
Traditional Image

This paper presents a method of extracting traffic lines from image images by GAN. Compared with the traditional image detection methods, the counter neural network does not need repeated sampling of Markov chain and adopts the method of backward propagation. Therefore, when detecting the image, GAN do not need to be updated with samples; it can produce better quality samples, express more clearly. Experimental results show that the method has strong generalization ability, fast recognition speed and high accuracy.

scholarly journals Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease

2020 ◽  
Author(s):  
Mohammadreza Zandehshahvar ◽  
Marly van Assen ◽  
Hossein Maleki ◽  
Yashar Kiarashi ◽  
Carlo N. De Cecco ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Area Under Curve ◽  
Valuable Insight ◽  
X Rays ◽  
Course Of Disease ◽  
New Approach ◽  
Clinical Model ◽  
Average Area ◽  
Learning Technique

ABSTRACTWe report a new approach using artificial intelligence to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with average area under curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in single patients and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in early stages. This will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.

scholarly journals Crack Detection on Concrete Surfaces Using Deep Encoder-Decoder Convolutional Neural Network: A Comparison Study Between U-Net and DeepLabV3+

2021 ◽  
Vol 7 (3) ◽  
pp. 323
Author(s):  
Patrick Nicholas Hadinata ◽  
Djoni Simanta ◽  
Liyanto Eddy ◽  
Kohei Nagai
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Crack Detection ◽  
Detection Methods ◽  
Processing Unit ◽  
Validation Data ◽  
Efficient System ◽  
External Data ◽  
Segmentation Accuracy

Maintenance of infrastructures is a crucial activity to ensure safety using crack detection methods on concrete structures. However, most practice of crack detection is carried out manually, which is unsafe, highly subjective, and time-consuming. Therefore, a more accurate and efficient system needs to be implemented using artificial intelligence. Convolutional neural network (CNN), a subset of artificial intelligence, is used to detect cracks on concrete surfaces through semantic image segmentation. The purpose of this research is to compare the effectiveness of cutting-edge encoder-decoder architectures in detecting cracks on concrete surfaces using U-Net and DeepLabV3+ architectures with potential in biomedical, and sparse multiscale image segmentations, respectively. Neural networks were trained using cloud computing with a high-performance Graphics Processing Unit NVIDIA Tesla V100 and 27.4 GB of RAM. This study used internal and external data. Internal data consisted of simple cracks and were used as the training and validation data. Meanwhile, external data consisted of more complex cracks, which were used for further testing. Both architectures were compared based on four evaluation metrics in terms of accuracy, F1, precision, and recall. U-Net achieved segmentation accuracy = 96.57%, F1 = 87.55%, precision = 88.15%, and recall = 88.94%, while DeepLabV3+ achieved segmentation accuracy = 96.47%, F1 = 85.29%, precision = 92.07%, and recall = 81.84%. Experiment results (internal and external data) indicated that both architectures were accurate and effective in segmenting cracks. Additionally, U-Net and DeepLabV3+ exceeded the performance of previously tested architecture, namely FCN.

Non-symbolic approaches to artificial intelligence and the mind

1994 ◽  
Vol 349 (1689) ◽  
pp. 87-102 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Nervous System ◽  
Computational Neuroscience ◽  
External World ◽  
The Mind ◽  
Symbolic Approach ◽  
Different Levels ◽  
New Algorithms

Current theories of artificial intelligence and the mind are dominated by the notion that thinking involves the manipulation of symbols. The symbols are intended to have a specific semantics in the sense that they represent concepts referring to objects in the external world and they conform to a syntax, being operated on by specific rules. I describe three alternative, non-symbolic approaches, each with a different emphasis but all using the same underlying computational model. This is a network of interacting computing units, a unit representing a nerve cell to a greater or lesser degree of fidelity in the different approaches. Computational neuroscience emphasizes the development and functioning of the nervous system; the approach of neural networks examines new algorithms for specific applications in, for example, pattern recognition and classification; according to the sub-symbolic approach , concepts are built up of entities called sub-symbols, which are the activities of individual processing units in a neural network. A frequently debated question is whether theories formulated at the subsymbolic level are ‘mere implementations’ of symbolic ones. I describe recent work due to Foster, who proposes that it is valid to view a system at many different levels of description and that, whereas any theory may have many different implementations, in general sub-symbolic theories may not be implementations of symbolic ones.

scholarly journals Toward understanding COVID-19 pneumonia: a deep-learning-based approach for severity analysis and monitoring the disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammadreza Zandehshahvar ◽  
Marly van Assen ◽  
Hossein Maleki ◽  
Yashar Kiarashi ◽  
Carlo N. De Cecco ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Area Under The Curve ◽  
Valuable Insight ◽  
X Rays ◽  
Single Patient ◽  
New Approach ◽  
Clinical Model ◽  
Average Area ◽  
Learning Technique

AbstractWe report a new approach using artificial intelligence (AI) to study and classify the severity of COVID-19 using 1208 chest X-rays (CXRs) of 396 COVID-19 patients obtained through the course of the disease at Emory Healthcare affiliated hospitals (Atlanta, GA, USA). Using a two-stage transfer learning technique to train a convolutional neural network (CNN), we show that the algorithm is able to classify four classes of disease severity (normal, mild, moderate, and severe) with the average Area Under the Curve (AUC) of 0.93. In addition, we show that the outputs of different layers of the CNN under dominant filters provide valuable insight about the subtle patterns in the CXRs, which can improve the accuracy in the reading of CXRs by a radiologist. Finally, we show that our approach can be used for studying the disease progression in a single patient and its influencing factors. The results suggest that our technique can form the foundation of a more concrete clinical model to predict the evolution of COVID-19 severity and the efficacy of different treatments for each patient through using CXRs and clinical data in the early stages of the disease. This use of AI to assess the severity and possibly predicting the future stages of the disease early on, will be essential in dealing with the upcoming waves of COVID-19 and optimizing resource allocation and treatment.

scholarly journals The application of the artificial neural network in analysing the spread quality

2018 ◽  
Vol 226 ◽  
pp. 04042
Author(s):  
Marko Petkovic ◽  
Marija Blagojevic ◽  
Vladimir Mladenovic
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Food Processing ◽  
New Approach ◽  
Confectionery Products ◽  
Speed Up ◽  
Artificial Neural

In this paper, we introduce a new approach in food processing using an artificial intelligence. The main focus is simulation of production of spreads and chocolate as representative confectionery products. This approach aids to speed up, model, optimize, and predict the parameters of food processing trying to increase quality of final products. An artificial intelligence is used in field of neural networks and methods of decisions.

Terra Mars: When Earth Shines on Mars through AI’s Imagination

Leonardo ◽  
2019 ◽  
Vol 52 (4) ◽  
pp. 357-363 ◽  
Author(s):  
Weili Shi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Satellite Imagery ◽  
New Approach ◽  
Visual Reference ◽  
Artificial Neural

Terra Mars presents artistic renderings of Mars with visual reference to our very own planet Earth. The author trained an artificial neural network with topographical data and satellite imagery of Earth so that it can learn the relation between them. The author then applied the trained model to topographical data of Mars to generate images that resemble satellite imagery of Earth. This project suggests a new approach to creative applications of artificial intelligence—using its capability of remapping to broaden the domain of artistic imagination.

NEURAL NETWORK SIMULATION: A NEW APPROACH TO RISK ASSESSMENT AND BUSINESS FORECASTING

1996 ◽  
Vol 19 (6) ◽  
pp. 50-54 ◽  
Author(s):  
Michael Peel ◽  
Nick Wilson
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Risk Assessment ◽  
Computer Software ◽  
New Approach ◽  
Financial Applications ◽  
Neural Architecture ◽  
Neural Network Simulation ◽  
Innovative Method ◽  
Business Forecasting

Neural Networks (NN's), one of the latest developments in computer software artificial intelligence, are an innovative method of simulating and analysing complex and changing systems of relationships. Originally developed to mimic the neural architecture and functioning of the human brain, NN techniques have recently been applied successfully in a wide variety of complex business and financial applications (Trippi and Turban, 1994).

scholarly journals Home Textile Pattern Emotion Labeling Using Deep Multi-View Feature Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Juan Yang ◽  
Yuanpeng Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Feature Learning ◽  
Experimental Results ◽  
Emotional Expressions ◽  
Retrieval Performance ◽  
Deep Feature ◽  
Feature Extractor ◽  
The Aesthetic

Different home textile patterns have different emotional expressions. Emotion evaluation of home textile patterns can effectively improve the retrieval performance of home textile patterns based on semantics. It can not only help designers make full use of existing designs and stimulate creative inspiration but also help users select designs and products that are more in line with their needs. In this study, we develop a three-stage framework for home textile pattern emotion labeling based on artificial intelligence. To be specific, first of all, three kinds of aesthetic features, i.e., shape, texture, and salient region, are extracted from the original home textile patterns. Then, a CNN (convolutional neural network)-based deep feature extractor is constructed to extract deep features from the aesthetic features acquired in the previous stage. Finally, a novel multi-view classifier is designed to label home textile patterns that can automatically learn the weight of each view. The three-stage framework is evaluated by our data and the experimental results show its promising performance in home textile patterns labeling.

scholarly journals Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

2017 ◽  
Vol 13 (3) ◽  
pp. 342 ◽  
Author(s):  
Alaá Rateb Mahmoud Al-shamasneh ◽  
Unaizah Hanum Binti Obaidellah
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Human Cancer ◽  
Imaging Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Inference System ◽  
Mortality And Morbidity ◽  
Artificial Intelligence Techniques

Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.

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