Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Researchers have discussed various defenses to these attacks to enhance model robustness, such as adversarial training and input preprocessing, along with mechanisms like data augmentation and uncertainty…

• NR
  National Research Foundation
• NR
  National Research Foundation of Korea

  Award: 2021R1A2C1011198
• MO
  Ministry of Science and ICT, South Korea

  Awards: IITP-2021-2020-0-01821, 2020-0-01821
• IF
  Institute for Information and Communications Technology Promotion

  Awards: IITP-2021-2020-0-01821, RS-2022-II220688, 2022-