scholarly journals A Comparative Study on the Privacy Risks of Face Recognition Libraries

2021 ◽  
Author(s):  
István Fábián ◽  
Gábor György Gulyás
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Rapid Development ◽  
Face Image ◽  
Human Face ◽  
Learning Models ◽  
Demographic Attributes ◽  
Computational Resources ◽  
Privacy Risks ◽  
Machine Learning Models

The rapid development of machine learning and the decreasing costs of computational resources has led to a widespread usage of face recognition. While this technology offers numerous benefits, it also poses new risks. We consider risks related to the processing of face embeddings, which are floating point vectors representing the human face in an identifying way. Previously, we showed that even simple machine learning models are capable of inferring demographic attributes from embeddings, leading to the possibility of re-identification attacks. This paper examines three popular Python libraries for face recognition, comparing their face detection performance and inspecting how much risk each library's embeddings pose regarding the aforementioned data leakage. Our experiments were conducted on a balanced face image dataset of different sexes and races, allowing us to discover biases in our results.

scholarly journals Fraudulent Face Image Detection

2020 ◽  
Vol 32 ◽  
pp. 03005
Author(s):  
Rahul Awhad ◽  
Saurabh Jayswal ◽  
Adesh More ◽  
Jyoti Kundale
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Face Image ◽  
Support Vector ◽  
Learning Models ◽  
Image Detection ◽  
Software Applications ◽  
Support Vector Classifier ◽  
The Face ◽  
Machine Learning Models

Due to the growing advancements in technology, many software applications are being developed to modify and edit images. Such software can be used to alter images. Nowadays, an altered image is so realistic that it becomes too difficult for a person to identify whether the image is fake or real. Such software applications can be used to alter the image of a person’s face also. So, it becomes very difficult to identify whether the image of the face is real or not. Our proposed system is used to identify whether the image of a face is fake or real. The proposed system makes use of machine learning. The system makes use of a convolution neural network and support vector classifier. Both these machine learning models are trained using real as well as fake images. Both these trained models will take an image as an input and will determine whether the image is fake or real.

scholarly journals Face Recognition System with Machine Learning

2021 ◽  
Vol 2089 (1) ◽  
pp. 012047
Author(s):  
Vuppu Padmakar ◽  
B V Ramana Murthy
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Open Source ◽  
Programming Language ◽  
Recognition System ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Face Recognition System ◽  
Machine Learning Models

Abstract This venture plans to give improved security by enabling a client to realize who is actually getting to the framework utilizing facial acknowledgment. The framework enables just approved clients to get entrance. Python is a programming language utilized alongside Machine learning methods and an open source library which is utilized to configuration, construct and train Machine learning models. Interface component is additionally accommodated unapproved clients to enroll to obtain entrance with the earlier authorization from the Admin.

scholarly journals Changing the HTS Paradigm: AI-Driven Iterative Screening for Hit Finding

2020 ◽  
pp. 247255522094949
Author(s):  
Gabriel H. S. Dreiman ◽  
Magda Bictash ◽  
Paul V. Fish ◽  
Lewis Griffin ◽  
Fredrik Svensson
Keyword(s):  
Machine Learning ◽  
High Throughput Screening ◽  
Practical Implementation ◽  
Learning Models ◽  
Screening Process ◽  
Return Rates ◽  
Screening Strategies ◽  
Computational Resources ◽  
Number Of Iterations ◽  
Machine Learning Models

Iterative screening is a process in which screening is done in batches, with each batch filled by using machine learning to select the most promising compounds from the library based on the previous results. We believe iterative screening is poised to enhance the screening process by improving hit finding while at the same time reducing the number of compounds screened. In addition, we see this process as a key enabler of next-generation high-throughput screening (HTS), which uses more complex assays that better describe the biology but demand more resource per screened compound. To demonstrate the utility of these methods, we retrospectively analyze HTS data from PubChem with a focus on machine learning–based screening strategies that can be readily implemented in practice. Our results show that over a variety of HTS experimental paradigms, an iterative screening setup that screens a total of 35% of the screening collection over as few as three iterations has a median return rate of approximately 70% of the active compounds. Increasing the portion of the library screened to 50% yields median returns of approximately 80% of actives. Using six iterations increases these return rates to 78% and 90%, respectively. The best results were achieved with machine learning models that can be run on a standard desktop. By demonstrating that the utility of iterative screening holds true even with a small number of iterations, and without requiring significant computational resources, we provide a roadmap for the practical implementation of these techniques in hit finding.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Sign in / Sign up

Export Citation Format

Share Document