no code implementations • 8 Sep 2023 • Qinghua Xu, Shaukat Ali, Tao Yue, Zaimovic Nedim, Inderjeet Singh
However, constructing a DT for anomaly detection in TCMS necessitates sufficient training data and extracting both chronological and context features with high quality.
no code implementations • 11 Apr 2023 • Inderjeet Singh, Kazuya Kakizaki, Toshinori Araki
In this work, we investigate the potential threat of adversarial examples to the security of face recognition systems.
no code implementations • 29 Nov 2022 • 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.
no code implementations • 23 Mar 2022 • Inderjeet Singh, Toshinori Araki, Kazuya Kakizaki
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.
no code implementations • 27 Dec 2021 • Inderjeet Singh, Nandyala Hemachandra
To the best of our knowledge, this is the first instance where a capsule network is analyzed for the anomaly detection task in a high-dimensional complex data setting.
no code implementations • 29 Sep 2021 • Inderjeet Singh, Satoru Momiyama, Kazuya Kakizaki, Toshinori Araki
This paper introduces a novel adversarial example generation method against face recognition systems (FRSs).
no code implementations • 14 Sep 2021 • Nitzan Guetta, Asaf Shabtai, Inderjeet Singh, Satoru Momiyama, Yuval Elovici
Deep learning face recognition models are used by state-of-the-art surveillance systems to identify individuals passing through public areas (e. g., airports).
no code implementations • 5 Jul 2021 • Ron Bitton, Nadav Maman, Inderjeet Singh, Satoru Momiyama, Yuval Elovici, Asaf Shabtai
Using the extension, security practitioners can apply attack graph analysis methods in environments that include ML components; thus, providing security practitioners with a methodological and practical tool for evaluating the impact and quantifying the risk of a cyberattack targeting an ML production system.