Are you for real? Decoding hyperrealistic AI-generated faces from neural activity

2020 ◽  
Author(s):  
Michoel L Moshel ◽  
Amanda K Robinson ◽  
Thomas A. Carlson ◽  
Tijl Grootswagers
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Brain Imaging ◽  
Neural Activity ◽  
Border Security ◽  
Fake News ◽  
The Real ◽  
Artificial Neural

Can we trust our eyes? Until recently, we rarely had to question whether what we see is indeed what exists, but this is changing. Artificial neural networks can now generate hyperrealistic images that challenge our perception of what is real. This new reality can have significant implications in cybersecurity, counterfeiting, fake news, and border security. We investigated how the human brain encodes and interprets hyperrealistic artificially generated images using behaviour and brain imaging. We found that we could reliably detect AI-generated fake images using neural activity, even though people could not consciously report seeing differences between real and fake images. Understanding this dissociation between brain and behaviour may be key in determining the 'real' in our new reality.

scholarly journals Optimization of a Steam Reforming Plant Modeled with Artificial Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1923
Author(s):  
Eduardo G. Pardo ◽  
Jaime Blanco-Linares ◽  
David Velázquez ◽  
Francisco Serradilla
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steam Reforming ◽  
Real Data ◽  
Memetic Algorithms ◽  
Black Box ◽  
Optimization Techniques ◽  
Ann Model ◽  
The Real ◽  
Artificial Neural

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.

scholarly journals Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 268 ◽  
Author(s):  
Ivaldo Tavares Júnior ◽  
Jonas Rocha ◽  
Ângelo Ebling ◽  
Antônio Chaves ◽  
José Zanuncio ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Model Fit ◽  
P Value ◽  
Diameter Class ◽  
The Real ◽  
Total Data ◽  
Artificial Neural ◽  
Clone Group

Equations to predict Eucalyptus timber volume are continuously updated, but most of them cannot be used for certain locations. Thus, equations of similar strata are applied to clonal plantations where trees cannot be felled to fit volumetric models. The objective of this study was to use linear regression and artificial neural networks (ANN) to reduce the number of trees sampled while maintaining the accuracy of commercial volume predictions with bark up to 4 cm in diameter at the top (v) of Eucalyptus clones. Two methods were evaluated in two scenarios: (a) regression model fit and ANN training with 80% of the data (533 trees) and per clone group with 80% of the trees in each group; and (b) model fit and ANN training with trees of only one clone group at ages two and three, with sample intensities of six, five, four, three, two, and one tree per diameter class. The real and predicted v averages did not differ in sample intensities from six to two trees per diameter class with different methods. The frequency distribution of individuals by volume class by the two methods (regression and ANN) compared to the real values were similar in scenarios (a) and (b) by the Kolmogorov–Smirnov test (p-value > 0.01). The application of ANN was more effective for total data analysis with non-linear behavior, without sampled environment stratification. The Prodan model also generates estimates with accuracy, and, among the regression models, is the best fit to the data. The volume with bark up to 4 cm in diameter at the top of Eucalyptus clones can be predicted with at least three trees per diameter class with regression (root mean square error in percentage, RMSE = 12.32%), and at least four trees per class with ANN (RMSE = 11.73%).

scholarly journals AN ARTIFICIAL NEURAL NETWORKS MODEL FOR THE ESTIMATION OF FORMWORK LABOUR / DIRBTINIŲ NEURONINIŲ TINKLŲ MODELIS, KURIO PASKIRTIS – SKAIČIUOTI KLOJINIAMS SKIRTO DARBO APIMTIS

2011 ◽  
Vol 17 (3) ◽  
pp. 340-347 ◽  
Author(s):  
S. Umit Dikmen ◽  
Murat Sonmez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Problem Solving ◽  
Human Brain ◽  
Field Measurements ◽  
Reinforced Concrete Frame ◽  
Concrete Frame ◽  
Frame Buildings ◽  
Actual Field ◽  
Artificial Neural

Artificial Neural Networks (ANN) is a problem solving technique imitating the basic working principles of the human brain. The formwork labour cost constitutes an important part within the costs of the reinforced concrete frame buildings. This study suggests a method based on artificial neural networks developed for estimating the required manhours for the formwork activity of such buildings. The introduced method has been verified in the study with reference to the test conducted involving two case studies. In all cases, the model produced results reasonably close to actual field measurements. The model is a simple and quick tool for the estimators and planners to aid them in their work. Santrauka Dirbtiniai neuroniniai tinklai (DNT) – tai problemų sprendimo metodas, imituojantis pagrindinius žmogaus smegenų veiklos principus. Statant gelžbetoninius karkasinius pastatus, nemažą sąnaudų dalį sudaro klojinių ruošimas. Šiame tyrime siūlomas dirbtiniais neuroniniais tinklais pagrįstas metodas, kurio paskirtis – apskaičiuoti, kiek žmogaus darbo valandų reikės ruošti klojinius tokiuose pastatuose. Pristatomas metodas tyrimo metu patikrintas remiantis bandymu, susijusiu su dviem atvejo tyrimais. Visais atvejais modelio pateikti rezultatai buvo gana artimi faktiniams matavimams. Modelis – tai paprastas ir greitai naudojamas įrankis, kuris pravers sąmatininkams ir planuotojams.

scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

scholarly journals Artificial Neural Networks and Human Brain: Survey of Improvement Possibilities of Learning

2015 ◽  
Vol 3 ◽  
pp. 228 ◽  
Author(s):  
Aleksejs Zorins ◽  
Peteris Grabusts
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Topology ◽  
Hybrid Methods ◽  
Learning Algorithms ◽  
Real Life ◽  
Real Problem ◽  
Brain Functions ◽  
Artificial Neural

<p class="R-AbstractKeywords">There are numerous applications of Artificial Neural Networks (ANN) at the present time and there are different learning algorithms, topologies, hybrid methods etc. It is strongly believed that ANN is built using human brain’s functioning principles but still ANN is very primitive and tricky way for real problem solving. In the recent years modern neurophysiology advanced to a big extent in understanding human brain functions and structure, however, there is a lack of this knowledge application to real ANN learning algorithms. Each learning algorithm and each network topology should be carefully developed to solve more or less complex problem in real life. One may say that almost each serious application requires its own network topology, algorithm and data pre-processing. This article presents a survey of several ways to improve ANN learning possibilities according to human brain structure and functioning, especially one example of this concept – neuroplasticity – automatic adaptation of ANN topology to problem domain.</p>

scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

Comprehensive Modelling of ANN

2021 ◽  
pp. 18-29
Author(s):  
Meghna Babubhai Patel ◽  
Jagruti N. Patel ◽  
Upasana M. Bhilota
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Human Brain ◽  
Processing System ◽  
Novel Structure ◽  
Pros And Cons ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Networks Structure

An artificial neural network (ANN) is an information processing modelling of the human brain inspired by the way biological nervous systems behave. There are about 100 billion neurons in the human brain. Each neuron has a connection point between 1,000 and 100,000. The key element of this paradigm is the novel structure of the information processing system. In the human brain, information is stored in such a way as to be distributed, and we can extract more than one piece of this information when necessary from our memory in parallel. We are not mistaken when we say that a human brain is made up of thousands of very powerful parallel processors. It is composed of a large number of highly interconnected processing elements (neurons) working in union to solve specific problems. ANN, like people, learns by example. The chapter includes characteristics of artificial neural networks, structure of ANN, elements of artificial neural networks, pros and cons of ANN.

scholarly journals Prediction of Intensive Care Cases for COVID-19 Pandemic in Malaysia: An Artificial Neural Networks Approach

2021 ◽  
Vol 16 ◽  
pp. 1-10
Author(s):  
Ahmad Afif Ahmarofi ◽  
Norhaslinda Zainal Abidin ◽  
Nerda Zura Zabidi
Keyword(s):  
Neural Networks ◽  
Intensive Care Unit ◽  
Artificial Neural Networks ◽  
Intensive Care ◽  
Facilities Management ◽  
High Demand ◽  
Ministry Of Health ◽  
The Real ◽  
Care Services ◽  
Artificial Neural

Coronavirus 2019 (COVID-19) pandemic in Malaysia is a part of the ongoing worldwide pandemic. The emergence of COVID-19 has led to high demand for intensive care services worldwide. However, the severity of COVID-19 patients that need intensive care unit (ICU) treatments requires details investigation. This study aims to predict the number of ICU cases due to COVID-19 disease in Malaysia. The prediction was done based on the data related to new, recovered, and treated cases which were collected from the website of the Ministry of Health Malaysia started from April until August 2020. Artificial Neural Networks Multilayers Perceptron Backpropagation (ANN-MLP-BPP) model was developed for predicting ICU cases based on the usage of the real set of data. The ANN-MLP-BPP model was validated by splitting the data into 80% for training and 20% for testing. The results show that with the increase in the number of undertreated cases, the number of predicted ICU will also be increased. The predicted ICU admission is almost equivalent to a 1 percent increment of the number of cases undertreated. These findings may help the frontline physicians in planning and handling the facilities management during the COVID-19 pandemic situation in the future.

Sign in / Sign up

Export Citation Format

Share Document