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20182023
most citedUniversal Adversarial Spoofing Attacks against Face Recognition

8 citations · 8 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2023

Simultaneous Adversarial Attacks On Multiple Face Recognition System Components

Inderjeet Singh, Kazuya Kakizaki, Toshinori Araki

In this work, we investigate the potential threat of adversarial examples to the security of face recognition systems. Although previous research has explored the adversarial risk…

cs.LG2022

Advancing Deep Metric Learning Through Multiple Batch Norms And Multi-Targeted Adversarial Examples

Inderjeet Singh, Kazuya Kakizaki, Toshinori Araki

Deep Metric Learning (DML) is a prominent field in machine learning with extensive practical applications that concentrate on learning visual similarities. It is known that inputs…

cs.CR2022

Powerful Physical Adversarial Examples Against Practical Face Recognition Systems

Inderjeet Singh, Toshinori Araki, Kazuya Kakizaki

It is well-known that the most existing machine learning (ML)-based safety-critical applications are vulnerable to carefully crafted input instances called adversarial examples (AX…

cs.CV2021★ 8 cited

Universal Adversarial Spoofing Attacks against Face Recognition

Takuma Amada, Seng Pei Liew, Kazuya Kakizaki +1

We assess the vulnerabilities of deep face recognition systems for images that falsify/spoof multiple identities simultaneously. We demonstrate that, by manipulating the deep featu…

cs.CV2021

On Brightness Agnostic Adversarial Examples Against Face Recognition Systems

Inderjeet Singh, Satoru Momiyama, Kazuya Kakizaki +1

This paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise…

cs.LG2019

Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems

Kazuya Kakizaki, Kosuke Yoshida

Thanks to recent advances in deep neural networks (DNNs), face recognition systems have become highly accurate in classifying a large number of face images. However, recent studies…