A5065449371- Speech and Audio Processing
- Face recognition and analysis
- Model Reduction and Neural Networks
- Image and Signal Denoising Methods
- Adversarial Robustness in Machine Learning
- Biometric Identification and Security
National Institute of Informatics
2024
Deep-learning-based identity management systems, such as face authentication are vulnerable to adversarial attacks. However, existing attacks typically designed for single-task purposes, which means they tailored exploit vulnerabilities unique the individual target rather than being adaptable multiple users or systems. This limitation makes them unsuitable certain attack scenarios, morphing, universal, transferable, and counter In this paper, we propose a multi-task algorithm called MTADV...
Face authentication systems have brought significant convenience and advanced developments, yet they become unreliable due to their sensitivity inconspicuous perturbations, such as adversarial attacks.Existing defenses often exhibit weaknesses when facing various attack algorithms adaptive attacks or compromise accuracy for enhanced security.To address these challenges, we developed a novel highly efficient non-deep-learning-based image filter called the Iterative Window Mean Filter (IWMF)...