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RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

Published in ICML, 2024

In this work, we designed a novel method to detect outliers in adversarial settings. We were able to achieve state-of-the-art results on various tasks of outlier detection by generating adaptive outliers and expose them while training the anomaly detector adversarially.

Authors: Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Ali Ansari, Sepehr Ghobadi, Masoud Hadi, Arshia Soltani Moakhar, Mohammad Azizmalayeri, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban
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Scanning Trojaned Models Using Out-of-Distribution Samples

Published in NeurIPS, 2024

In this work, we designed a trojan scanning method which is robust in various aspects, including trojan attack type, label mapping, and adversarial robustness of the classifier. We further propose a version of our work which requires no access samples from the model’s training distribution.

Authors: Hossein Mirzaei, Ali Ansari*, Bahar Dibaei Nia*, Mojtaba Nafez, Moein Madadi, Sepehr Rezaee, Zeinab Sadat Taghavi, Arad Maleki, Kian Shamsaie, Mahdi Hajialilue, Jafar Habibi, Mohammad Sabokrou, Mohammad Hossein Rohban
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Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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