Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases (2022)
- Authors:
- USP affiliated authors: GUTIERREZ, MARCO ANTONIO - EP ; TRAINA JUNIOR, CAETANO - ICMC ; TRAINA, AGMA JUCI MACHADO - ICMC ; AGUIAR, ERIKSON JÚLIO DE - ICMC ; MARCOMINI, KAREM DAIANE - ICMC ; QUIRINO, FELIPE ANTUNES - ICMC
- Unidades: EP; ICMC
- DOI: 10.1117/12.2611199
- Subjects: REDES NEURAIS; APRENDIZADO COMPUTACIONAL; RECONHECIMENTO DE IMAGEM; TECNOLOGIAS DA SAÚDE; RADIOGRAFIA; COVID-19
- Keywords: Adversarial attacks; deep neural networks; Fast Gradient Sign Method
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: International Society for Optical Engineering - SPIE
- Publisher place: Bellingham
- Date published: 2022
- Source:
- Título: Proceedings of SPIE
- ISSN: 1605-7422
- Volume/Número/Paginação/Ano: v. 12033, p. 120332P-1-120332P-7, 2022
- Conference titles: SPIE Medical Imaging
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
AGUIAR, Erikson Júlio de et al. Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases. Proceedings of SPIE. Bellingham: International Society for Optical Engineering - SPIE. Disponível em: https://doi.org/10.1117/12.2611199. Acesso em: 28 dez. 2025. , 2022 -
APA
Aguiar, E. J. de, Marcomini, K. D., Quirino, F. A., Gutierrez, M. A., Traina Junior, C., & Traina, A. J. M. (2022). Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases. Proceedings of SPIE. Bellingham: International Society for Optical Engineering - SPIE. doi:10.1117/12.2611199 -
NLM
Aguiar EJ de, Marcomini KD, Quirino FA, Gutierrez MA, Traina Junior C, Traina AJM. Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases [Internet]. Proceedings of SPIE. 2022 ; 12033 120332P-1-120332P-7.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1117/12.2611199 -
Vancouver
Aguiar EJ de, Marcomini KD, Quirino FA, Gutierrez MA, Traina Junior C, Traina AJM. Evaluation of the impact of physical adversarial attacks on deep learning models for classifying covid cases [Internet]. Proceedings of SPIE. 2022 ; 12033 120332P-1-120332P-7.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1117/12.2611199 - Security and privacy in machine learning for health systems: strategies and challenges
- RADAR-MIX: how to uncover adversarial attacks in medical image analysis through explainability
- A deep learning approach for COVID-19 screening and localization on chest x-ray images
- SentinelAdvMedical: toward adversarial attacks detection on medical image classification via Out-Of-Distribution strategies
- MedTimeSplit: continual dataset partitioning to mimic real-world settings for federated learning on Non-IID medical image data
- Assessing vulnerabilities of deep learning explainability in medical image analysis under adversarial settings
- Data augmentation for medical image segmentation: a comparative analysis of traditional techniques and synthetic data generation
- DEELE-Rad: exploiting deep radiomics features in deep learning models using COVID-19 chest X-ray images
- Caracterização de lesões em imagens digitais de ultrassonografia e elastografia da mama utilizando técnicas inteligentes
- Recuperação de imagens médicas por conteúdo em um sistema de gerenciamento de banco de dados de código livre
Informações sobre o DOI: 10.1117/12.2611199 (Fonte: oaDOI API)
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