scholarly journals Fighting Deepfakes Using Body Language Analysis

2021 ◽  
Author(s):  
Robail Yasrab ◽  
Wanqi Jiang ◽  
Adnan Riaz
Keyword(s):  
Open Source Software ◽  
Body Language ◽  
Classification Model ◽  
Upper Body ◽  
Final Model ◽  
Ulterior Motive ◽  
The People ◽  
Language Analysis ◽  
Barrier To Entry ◽  
Lstm Network

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

scholarly journals Fighting Deepfakes Using Body Language Analysis

Forecasting ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 303-321
Author(s):  
Robail Yasrab ◽  
Wanqi Jiang ◽  
Adnan Riaz
Keyword(s):  
Open Source Software ◽  
Detection Method ◽  
Body Language ◽  
Classification Model ◽  
Upper Body ◽  
Final Model ◽  
Ulterior Motive ◽  
Language Analysis ◽  
Barrier To Entry ◽  
Lstm Network

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

scholarly journals Fighting Deepfakes Using Body Language Analysis

2021 ◽  
Author(s):  
Robail Yasrab ◽  
Wanqi Jiang ◽  
Adnan Riaz
Keyword(s):  
Open Source Software ◽  
Body Language ◽  
Classification Model ◽  
Upper Body ◽  
Final Model ◽  
The Public ◽  
Ulterior Motive ◽  
Language Analysis ◽  
Barrier To Entry ◽  
Lstm Network

Recent improvements in deepfake creation made deepfake videos more realistic. Open-source software has also made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the public privacy. It is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of the countries and the world. Research into automated detection for deepfaked media is therefore essential for public safety. We propose in this work the use of upper body language analysis for deepfake detection. Specifically, a many-to-one LSTM network was designed and trained as a classification model is trained for deepfake detection. Different models trained using various hyper-parameters to build a final model with benchmark accuracy. We achieve 94.39% accuracy on a test deepfake set. The experimental results show that upper body language can effectively provide identification and deepfake detection.

KArSL

2021 ◽  
Vol 20 (1) ◽  
pp. 1-19
Author(s):  
Ala Addin I. Sidig ◽  
Hamzah Luqman ◽  
Sabri Mahmoud ◽  
Mohamed Mohandes
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Markov Models ◽  
Body Language ◽  
Arab Countries ◽  
Microsoft Kinect ◽  
Classification Model ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the challenges of the automatic recognition of Arabic Sign language. This article introduces KArSL database for ArSL, consisting of 502 signs that cover 11 chapters of ArSL dictionary. Signs in KArSL database are performed by three professional signers, and each sign is repeated 50 times by each signer. The database is recorded using state-of-art multi-modal Microsoft Kinect V2. We also propose three approaches for sign language recognition using this database. The proposed systems are Hidden Markov Models, deep learning images’ classification model applied on an image composed of shots of the video of the sign, and attention-based deep learning captioning system. Recognition accuracies of these systems indicate their suitability for such a large number of Arabic signs. The techniques are also tested on a publicly available database. KArSL database will be made freely available for interested researchers.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

scholarly journals 3D for the people: multi-camera motion capture in the field with consumer-grade cameras and open source software

Biology Open ◽  
2016 ◽  
Vol 5 (9) ◽  
pp. 1334-1342 ◽  
Author(s):  
Brandon E. Jackson ◽  
Dennis J. Evangelista ◽  
Dylan D. Ray ◽  
Tyson L. Hedrick
Keyword(s):  
Open Source ◽  
Motion Capture ◽  
Open Source Software ◽  
Camera Motion ◽  
The People

A Novel ECG Automatic Detection Using LongShort-Term Memory Network and Internet of Things Technology

2021 ◽  
Vol 11 (6) ◽  
pp. 1592-1598
Author(s):  
Xufei Liu
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Internet Of Things ◽  
Network Structure ◽  
Recognition Rate ◽  
Classification Model ◽  
Data Set ◽  
Public Data ◽  
Lstm Network ◽  
Internet Of Things Technology

The early detection of cardiovascular diseases based on electrocardiogram (ECG) is very important for the timely treatment of cardiovascular patients, which increases the survival rate of patients. ECG is a visual representation that describes changes in cardiac bioelectricity and is the basis for detecting heart health. With the rise of edge machine learning and Internet of Things (IoT) technologies, small machine learning models have received attention. This study proposes an ECG automatic classification method based on Internet of Things technology and LSTM network to achieve early monitoring and early prevention of cardiovascular diseases. Specifically, this paper first proposes a single-layer bidirectional LSTM network structure. Make full use of the timing-dependent features of the sampling points before and after to automatically extract features. The network structure is more lightweight and the calculation complexity is lower. In order to verify the effectiveness of the proposed classification model, the relevant comparison algorithm is used to verify on the MIT-BIH public data set. Secondly, the model is embedded in a wearable device to automatically classify the collected ECG. Finally, when an abnormality is detected, the user is alerted by an alarm. The experimental results show that the proposed model has a simple structure and a high classification and recognition rate, which can meet the needs of wearable devices for monitoring ECG of patients.

scholarly journals Garment Categorization Using Data Mining Techniques

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 984
Author(s):  
Sheenam Jain ◽  
Vijay Kumar
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Random Forest ◽  
Good Deal ◽  
Classification Model ◽  
Upper Body ◽  
Whole Body ◽  
Product Data ◽  
Soft Classification ◽  
Using Data

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Open Source Software Communities

2011 ◽  
pp. 363-367
Author(s):  
Kevin Carillo ◽  
Chitu Okoli
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Development Model ◽  
The People ◽  
Develop Software ◽  
Working Together ◽  
Software Development Model ◽  
Key Aspects ◽  
Open Source Communities

Open source software (OSS) development has continued to appear as a puzzling and enigmatic phenomenon and has drawn increasing attention as its importance has grown. Relying upon an alternative way to develop and to distribute software, open source communities have been able to challenge and often outperform proprietary software by enabling better reliability, lower costs, shorter development times, and a higher quality of code (Raymond, 2004). Behind the software is a mass of people working together in loose coordination, even portrayed as a rowdy marketplace (Raymond, 2001, p. 1): No quiet, reverent cathedral-building here—rather, the Linux community seemed to resemble a great babbling bazaar of differing agendas and approaches … out of which a coherent and stable system seemingly emerges only by a succession of miracles. More precisely, the people behind open source projects have been defined as: “Internet-based communities of software developers who voluntarily collaborate in order to develop software that they or their organizations need” (von Krogh, 2003, p. 14). In contrast to the sacred cathedral-like software development model that gave birth to most commercial and proprietary systems, such bazaar-like communities seem to have based their success on a pseudo-anarchic type of collaboration and developers’ interaction (Raymond, 2001). However, in spite of the apparent disorganization of these bazaars, a closer look distinguishes common values and norms that rule them, specific roles that can be identified, similar motives shared by people, and practices that follow patterns. This article highlights key aspects of what forms the communities that support these projects.

scholarly journals Assessing Egocentered Social Networks in formr: A Tutorial

2020 ◽  
Author(s):  
Louisa Marie Reins ◽  
Ruben C. Arslan ◽  
Tanja M. Gerlach
Keyword(s):  
Social Networks ◽  
Social Relationships ◽  
Open Source Software ◽  
Functional Study ◽  
Additional Information ◽  
Social Network Data ◽  
Psychological Science ◽  
The People ◽  
Interaction Partners ◽  
Set Up

In psychological science, egocentered social networks are assessed to investigate the patterning and development of social relationships. In this approach, a focal individual is typically asked to report the people they interact with in specific contexts and to provide additional information on those interaction partners and the relationships with them. While egocentered social networks hold considerable promise to investigate various interesting questions from psychology and beyond, their implementation can be challenging. This tutorial provides researchers with detailed instructions on how to set up a study involving egocentered social networks online using the open-source software formr. By including a fully functional study template for the assessment of social networks and extensions to this design, we hope to equip researchers from different backgrounds with the tools necessary to collect social network data tailored to their research needs.

scholarly journals Predicting the Visual Focus of Attention in Multi-Person Discussion Videos

2019 ◽  
Author(s):  
Chongyang Bai ◽  
Srijan Kumar ◽  
Jure Leskovec ◽  
Miriam Metzger ◽  
Jay F. Nunamaker ◽  
...  
Keyword(s):  
Deception Detection ◽  
Focus Of Attention ◽  
Classification Model ◽  
Collective Classification ◽  
Popular Resistance ◽  
Personal Relation ◽  
Attention Focus ◽  
The People ◽  
Collective Attention ◽  
Speech Transcription

Visual focus of attention in multi-person discussions is a crucial nonverbal indicator in tasks such as inter-personal relation inference, speech transcription, and deception detection. However, predicting the focus of attention remains a challenge because the focus changes rapidly, the discussions are highly dynamic, and the people's behaviors are inter-dependent. Here we propose ICAF (Iterative Collective Attention Focus), a collective classification model to jointly learn the visual focus of attention of all people. Every person is modeled using a separate classifier. ICAF models the people collectively---the predictions of all other people's classifiers are used as inputs to each person's classifier. This explicitly incorporates inter-dependencies between all people's behaviors. We evaluate ICAF on a novel dataset of 5 videos (35 people, 109 minutes, 7604 labels in all) of the popular Resistance game and a widely-studied meeting dataset with supervised prediction. See our demo at https://cs.dartmouth.edu/dsail/demos/icaf. ICAF outperforms the strongest baseline by 1%--5% accuracy in predicting the people's visual focus of attention. Further, we propose a lightly supervised technique to train models in the absence of training labels. We show that light-supervised ICAF performs similar to the supervised ICAF, thus showing its effectiveness and generality to previously unseen videos.

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