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Simultaneous Adversarial Attacks On Multiple Face Recognition System Components
In this work, we investigate the potential threat of adversarial examples to the security of face recognition systems.
Advancing Deep Metric Learning Through Multiple Batch Norms And Multi-Targeted Adversarial Examples
Deep Metric Learning (DML) is a prominent field in machine learning with extensive practical applications that concentrate on learning visual similarities.
Latent SHAP: Toward Practical Human-Interpretable Explanations
Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural networks.
Powerful Physical Adversarial Examples Against Practical Face Recognition Systems
Notably, our smoothness loss results in a 1. 17 and 1. 97 times better mean attack success rate (ASR) in physical white-box and black-box attacks, respectively.
Universal Adversarial Spoofing Attacks against Face Recognition
We assess the vulnerabilities of deep face recognition systems for images that falsify/spoof multiple identities simultaneously.
On Brightness Agnostic Adversarial Examples Against Face Recognition Systems
This paper introduces a novel adversarial example generation method against face recognition systems (FRSs).
