A Dissimilarity-Based Countermeasure for Detecting Replay Attacks in Speaker Verification Maria Eduarda Maciel Pinto, Alceu De Souza Britto, Andre Gustavo Hochuli Proceedings 2024 International Conference on Machine Learning and Applications Icmla 2024, 2024 Audio replay attacks present a significant challenge to automatic speaker verification systems (ASVs), emphasizing the need for effective detection methods. Traditionally, embedding-based approaches, such as those leveraging Convolutional Neural Networks (CNNs), have been used. However, dissimilarity-based methods emerge as a promising alternative, offering potential advantages in detecting subtle differences between genuine and spoofed audio. This study evaluates dissimilarity strategies for detecting genuine versus spoofed audio signals using a well-known benchmark dataset and established metrics, including accuracy and Equal Error Rate (EER). We provide a comparative performance assessment of various CNN architectures and dissimilarity strategies, finding that while dissimilarity approaches are competitive with embedding-based methods, the Dissimilarity Vectors strategy outperforms the Dissimilarity Space strategy.
Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers Paulo Luza Alves, André Hochuli, Luiz Eduardo de Oliveira, Paulo Lisboa de Almeida Proceedings 2024 International Conference on Machine Learning and Applications Icmla 2024, 2024 When deploying large-scale machine learning models for smart city applications, such as image-based parking lot monitoring, data often must be sent to a central server to perform classification tasks. This is challenging for the city's infrastructure, where image-based applications require transmitting large volumes of data, necessitating complex network and hardware infrastructures to process the data. To address this issue in image-based parking space classification, we propose creating a robust ensemble of classifiers to serve as Teacher models. These Teacher models are distilled into lightweight and specialized Student models that can be deployed directly on edge devices. The knowledge is distilled to the Student models through pseudo-labeled samples generated by the Teacher model, which are utilized to fine-tune the Student models on the target scenario. Our results show that the Student models, with 26 times fewer parameters than the Teacher models, achieved an average accuracy of 96.6 % on the target test datasets, surpassing the Teacher models, which attained an average accuracy of 95.3 %.
Deep Single Models vs. Ensembles: Insights for a Fast Deployment of Parking Monitoring Systems Andre Gustavo Hochuli, Jean Paul Barddal, Gillian Cezar Palhano, Leonardo Matheus Mendes, Paulo Ricardo Lisboa de Almeida Proceedings 22nd IEEE International Conference on Machine Learning and Applications Icmla 2023, 2023 Searching for available parking spots in high-density urban centers is a stressful task for drivers that can be mitigated by systems that know in advance the nearest parking space available. To this end, image-based systems offer cost advantages over other sensor-based alternatives (e.g., ultrasonic sensors), requiring less physical infrastructure for installation and maintenance. Despite recent deep learning advances, de-ploying intelligent parking monitoring is still a challenge since most approaches involve collecting and labeling large amounts of data, which is laborious and time-consuming. Our study aims to uncover the challenges in creating a global framework, trained using publicly available labeled parking lot images, that performs accurately across diverse scenarios, enabling the parking space monitoring as a ready-to-use system to deploy in a new environment. Through exhaustive experiments involving different datasets and deep learning architectures, including fusion strategies and ensemble methods, we found that models trained on diverse datasets can achieve 95% accuracy without the burden of data annotation and model training on the target parking lot.
Vehicle Occurrence-Based Parking Space Detection Paulo R. Lisboa de Almeida, Jeovane Honório Alves, Luiz S. Oliveira, Andre Gustavo Hochuli, João V. Fröhlich, et al. Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, 2023 Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. Computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled, impacting the cost and feasibility of deployment. To fill this gap, this work presents an automatic parking space detection method, which receives a sequence of images of a parking lot and returns a list of coordinates identifying the detected parking spaces. The proposed method employs instance segmentation to identify cars and, using vehicle occurrence, generate a heat map of parking spaces. The results using twelve different subsets from the PKLot and CNRPark-EXT parking lot datasets show that the method achieved an AP25 score up to 95.60% and AP50 score up to 79.90%.
Combining Muti-Layer Features For Plant Species Classification in a Siamese Network Matheus Moresco, Alceu De S. Britto, Yandre M. G. Costa, Luciano J. Senger, Andre G. Hochuli Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, 2022 The plant species classification using leaf images is a challenge due to the lack of annotation, imbalanced classes and similarities in the data representation. For such problems, Siamese Neural Networks (SNN’s) have been used to overcome these bottlenecks in several contexts. In light of this, this work evaluates different architectures trained in Siamese manner for classifying plant species from the leaf image. Besides, we combined features from the intermediate convolutional layers to improve representations. Experiments on the well-known Flavia and MalayaKew databases have shown that the fusion of intermediate features results in a relevant gain in performance.
Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems Andre G. Hochuli, Alceu S. Britto, Paulo R. L. de Almeida, Williams B. S. Alves, Fabio M. C. Cagni Proceedings of the International Joint Conference on Neural Networks, 2022 When using vision-based approaches to classify individual parking spaces between occupied and empty, human experts often need to annotate the locations and label a training set containing images collected in the target parking lot to fine-tune the system. We propose investigating three annotation types (polygons, bounding boxes, and fixed-size squares), providing different data representations of the parking spaces. The rationale is to elucidate the best trade-off between handcraft annotation precision and model performance. We also investigate the number of annotated parking spaces necessary to fine-tune a pre-trained model in the target parking lot. Experiments using the PKLot dataset show that it is possible to fine-tune a model to the target parking lot with less than 1,000 labeled samples, using low precision annotations such as fixed-size squares.
End-to-End Approach for Recognition of Historical Digit Strings Mengqiao Zhao, Andre Gustavo Hochuli, Abbas Cheddad Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2021
Detection and classification of human movements in video scenes A. G. Hochuli, L. E. S. Oliveira, A. S. Britto, A. L. Koerich Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2007
RECENT SCHOLAR PUBLICATIONS
A Generative Domain Adaptation Scheme for Swift Deployment of Parking Monitoring Systems AMF dos Santos, PRL de Almeida, JP Barddal, AG Hochuli Brazilian Conference on Intelligent Systems, 34-49 , 2025 2025.0
Towards An Unsupervised Deep Representations for Plant Species Recognition DH Presner, AG Hochuli, A de Souza Britto 2025 International Joint Conference on Neural Networks (IJCNN), 1-7 , 2025 2025.0
Representation ensemble learning applied to facial expression recognition BR Delazeri, AG Hochuli, JP Barddal, AL Koerich, AS Britto Jr Neural Computing and Applications 37 (1), 417-438 , 2025 2025.0 Citations: 3
A Dissimilarity-Based Countermeasure for Detecting Replay Attacks in Speaker Verification MEM Pinto, ADS Britto, AG Hochuli 2024 International Conference on Machine Learning and Applications (ICMLA … , 2024 2024.0
Optimizing parking space classification: Distilling ensembles into lightweight classifiers PL Alves, A Hochuli, LE de Oliveira, PL de Almeida 2024 International Conference on Machine Learning and Applications (ICMLA … , 2024 2024.0 Citations: 5
Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers P Luza Alves, A Hochuli, LE de Oliveira, P Lisboa de Almeida arXiv e-prints, arXiv: 2410.14705 , 2024 2024.0
Deep single models vs. ensembles: Insights for a fast deployment of parking monitoring systems AG Hochuli, JP Barddal, GC Palhano, LM Mendes, PRL de Almeida 2023 International Conference on Machine Learning and Applications (ICMLA … , 2023 2023.0 Citations: 7
Vehicle occurrence-based parking space detection PRL de Almeida, JH Alves, LS Oliveira, AG Hochuli, JV Fröhlich, ... 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC … , 2023 2023.0 Citations: 15
Vehicle Occurrence-based Parking Space Detection PR Lisboa de Almeida, J Honório Alves, LS Oliveira, AG Hochuli, ... arXiv e-prints, arXiv: 2306.09940 , 2023 2023.0
Combining muti-layer features for plant species classification in a Siamese network M Moresco, ADS Britto, YMG Costa, LJ Senger, AG Hochuli 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC … , 2022 2022.0 Citations: 7
Evaluation of different annotation strategies for deployment of parking spaces classification systems AG Hochuli, AS Britto, PRL de Almeida, WBS Alves, FMC Cagni 2022 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2022 2022.0 Citations: 10
End-to-end approach for recognition of historical digit strings M Zhao, AG Hochuli, A Cheddad International Conference on Document Analysis and Recognition, 595-609 , 2021 2021.0 Citations: 5
A comprehensive comparison of end-to-end approaches for handwritten digit string recognition AG Hochuli, AS Britto Jr, DA Saji, JM Saavedra, R Sabourin, LS Oliveira Expert Systems with Applications 165, 114196 , 2021 2021.0 Citations: 19
An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings AG Hochuli, AS Britto Jr, JP Barddal, LES Oliveira, R Sabourin 2020 International Joint Conference on Neural Networks (IJCNN) , 2020 2020.0 Citations: 18
Handwritten digit segmentation: Is it still necessary? AG Hochuli, LS Oliveira, AS Britto Jr, R Sabourin Pattern Recognition 78, 1-11 , 2018 2018.0 Citations: 61
Segmentation-Free Approaches for Handwritten Numeral String Recognition AG Hochuli, LS Oliveira, AS Britto, R Sabourin 2018 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2018 2018.0 Citations: 15
Detection and classification of human movements in video scenes AG Hochuli, LES Oliveira, AS Britto Jr, AL Koerich Pacific-Rim Symposium on Image and Video Technology, 678-691 , 2007 2007.0 Citations: 6
Detection of non-conventional events on video scenes AG Hochuli, AS Britto, AL Koerich 2007 IEEE International Conference on Systems, Man and Cybernetics, 302-307 , 2007 2007.0 Citations: 1
Detecçao de Eventos Nao Convencionais em Cenas de Vıdeo Utilizando Vetores de Caracterısticas AG Hochuli 2007.0
SEGMENTAÇÃO AUTOMATIZADA DE VAGAS DE ESTACIONAMENTO1 JV Fröhlich, PRL de Almeida, AG Hochuli, R Augusto
MOST CITED SCHOLAR PUBLICATIONS
Handwritten digit segmentation: Is it still necessary? AG Hochuli, LS Oliveira, AS Britto Jr, R Sabourin Pattern Recognition 78, 1-11 , 2018 2018.0 Citations: 61
A comprehensive comparison of end-to-end approaches for handwritten digit string recognition AG Hochuli, AS Britto Jr, DA Saji, JM Saavedra, R Sabourin, LS Oliveira Expert Systems with Applications 165, 114196 , 2021 2021.0 Citations: 19
An End-to-End Approach for Recognition of Modern and Historical Handwritten Numeral Strings AG Hochuli, AS Britto Jr, JP Barddal, LES Oliveira, R Sabourin 2020 International Joint Conference on Neural Networks (IJCNN) , 2020 2020.0 Citations: 18
Vehicle occurrence-based parking space detection PRL de Almeida, JH Alves, LS Oliveira, AG Hochuli, JV Fröhlich, ... 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC … , 2023 2023.0 Citations: 15
Segmentation-Free Approaches for Handwritten Numeral String Recognition AG Hochuli, LS Oliveira, AS Britto, R Sabourin 2018 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2018 2018.0 Citations: 15
Evaluation of different annotation strategies for deployment of parking spaces classification systems AG Hochuli, AS Britto, PRL de Almeida, WBS Alves, FMC Cagni 2022 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2022 2022.0 Citations: 10
Deep single models vs. ensembles: Insights for a fast deployment of parking monitoring systems AG Hochuli, JP Barddal, GC Palhano, LM Mendes, PRL de Almeida 2023 International Conference on Machine Learning and Applications (ICMLA … , 2023 2023.0 Citations: 7
Combining muti-layer features for plant species classification in a Siamese network M Moresco, ADS Britto, YMG Costa, LJ Senger, AG Hochuli 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC … , 2022 2022.0 Citations: 7
Detection and classification of human movements in video scenes AG Hochuli, LES Oliveira, AS Britto Jr, AL Koerich Pacific-Rim Symposium on Image and Video Technology, 678-691 , 2007 2007.0 Citations: 6
Optimizing parking space classification: Distilling ensembles into lightweight classifiers PL Alves, A Hochuli, LE de Oliveira, PL de Almeida 2024 International Conference on Machine Learning and Applications (ICMLA … , 2024 2024.0 Citations: 5
End-to-end approach for recognition of historical digit strings M Zhao, AG Hochuli, A Cheddad International Conference on Document Analysis and Recognition, 595-609 , 2021 2021.0 Citations: 5
Representation ensemble learning applied to facial expression recognition BR Delazeri, AG Hochuli, JP Barddal, AL Koerich, AS Britto Jr Neural Computing and Applications 37 (1), 417-438 , 2025 2025.0 Citations: 3
Detection of non-conventional events on video scenes AG Hochuli, AS Britto, AL Koerich 2007 IEEE International Conference on Systems, Man and Cybernetics, 302-307 , 2007 2007.0 Citations: 1
A Generative Domain Adaptation Scheme for Swift Deployment of Parking Monitoring Systems AMF dos Santos, PRL de Almeida, JP Barddal, AG Hochuli Brazilian Conference on Intelligent Systems, 34-49 , 2025 2025.0
Towards An Unsupervised Deep Representations for Plant Species Recognition DH Presner, AG Hochuli, A de Souza Britto 2025 International Joint Conference on Neural Networks (IJCNN), 1-7 , 2025 2025.0
A Dissimilarity-Based Countermeasure for Detecting Replay Attacks in Speaker Verification MEM Pinto, ADS Britto, AG Hochuli 2024 International Conference on Machine Learning and Applications (ICMLA … , 2024 2024.0
Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers P Luza Alves, A Hochuli, LE de Oliveira, P Lisboa de Almeida arXiv e-prints, arXiv: 2410.14705 , 2024 2024.0
Vehicle Occurrence-based Parking Space Detection PR Lisboa de Almeida, J Honório Alves, LS Oliveira, AG Hochuli, ... arXiv e-prints, arXiv: 2306.09940 , 2023 2023.0
Detecçao de Eventos Nao Convencionais em Cenas de Vıdeo Utilizando Vetores de Caracterısticas AG Hochuli 2007.0
SEGMENTAÇÃO AUTOMATIZADA DE VAGAS DE ESTACIONAMENTO1 JV Fröhlich, PRL de Almeida, AG Hochuli, R Augusto
