Search Results for author: Marius Popescu

Found 21 papers, 8 papers with code

VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web

no code implementations7 Jul 2022 Andrei Manolache, Florin Brad, Antonio Barbalau, Radu Tudor Ionescu, Marius Popescu

The DarkWeb represents a hotbed for illicit activity, where users communicate on different market forums in order to exchange goods and services.

Authorship Verification

A realistic approach to generate masked faces applied on two novel masked face recognition data sets

1 code implementation3 Sep 2021 Tudor Mare, Georgian Duta, Mariana-Iuliana Georgescu, Adrian Sandru, Bogdan Alexe, Marius Popescu, Radu Tudor Ionescu

We propose a method for enhancing data sets containing faces without masks by creating synthetic masks and overlaying them on faces in the original images.

Face Recognition

EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks

1 code implementation12 Jul 2021 Andrei Ilie, Marius Popescu, Alin Stefanescu

We propose $\textbf{EvoBA}$ as a query-efficient $L_0$ black-box adversarial attack which, together with the aforementioned methods, can serve as a generic tool to assess the empirical robustness of image classifiers.

Adversarial Attack

Anomaly Detection in Video via Self-Supervised and Multi-Task Learning

1 code implementation CVPR 2021 Mariana-Iuliana Georgescu, Antonio Barbalau, Radu Tudor Ionescu, Fahad Shahbaz Khan, Marius Popescu, Mubarak Shah

To the best of our knowledge, we are the first to approach anomalous event detection in video as a multi-task learning problem, integrating multiple self-supervised and knowledge distillation proxy tasks in a single architecture.

Abnormal Event Detection In Video Anomaly Detection In Surveillance Videos +4

Black-Box Ripper: Copying black-box models using generative evolutionary algorithms

1 code implementation NeurIPS 2020 Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu

To generate useful data samples for training the student, our framework (i) learns to generate images on a proxy data set (with images and classes different from those used to train the black-box) and (ii) applies an evolutionary strategy to make sure that each generated data sample exhibits a high response for a specific class when given as input to the black box.

A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI

1 code implementation6 Jun 2020 Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu

In this work, we focus on explainable AI and propose a novel generic and model-agnostic framework for synthesizing input exemplars that maximize a desired response from a machine learning model.

Self-Supervised Representation Learning on Document Images

no code implementations18 Apr 2020 Adrian Cosma, Mihai Ghidoveanu, Michael Panaitescu-Liess, Marius Popescu

This work analyses the impact of self-supervised pre-training on document images in the context of document image classification.

Classification Document Image Classification +2

Local Learning with Deep and Handcrafted Features for Facial Expression Recognition

no code implementations29 Apr 2018 Mariana-Iuliana Georgescu, Radu Tudor Ionescu, Marius Popescu

We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve state-of-the-art results in facial expression recognition.

Ranked #3 on Facial Expression Recognition (FER) on FER2013 (using extra training data)

Facial Expression Recognition (FER) General Classification

Detecting abnormal events in video using Narrowed Normality Clusters

no code implementations12 Jan 2018 Radu Tudor Ionescu, Sorina Smeureanu, Marius Popescu, Bogdan Alexe

To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality.

Anomaly Detection Event Detection +1

Can string kernels pass the test of time in Native Language Identification?

no code implementations WS 2017 Radu Tudor Ionescu, Marius Popescu

While most of our kernels are based on character p-grams (also known as n-grams) extracted from essays or speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided by the shared task organizers.

Native Language Identification

UnibucKernel: An Approach for Arabic Dialect Identification Based on Multiple String Kernels

no code implementations WS 2016 Radu Tudor Ionescu, Marius Popescu

Our approach is shallow and simple, but the empirical results obtained in the ADI Shared Task prove that it achieves very good results.

Dialect Identification Text Categorization

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