1 code implementation • 18 Oct 2023 • Zhengyu Zhao, Hanwei Zhang, Renjue Li, Ronan Sicre, Laurent Amsaleg, Michael Backes, Qi Li, Chao Shen
Transferable adversarial examples raise critical security concerns in real-world, black-box attack scenarios.
1 code implementation • 17 Nov 2022 • Zhengyu Zhao, Hanwei Zhang, Renjue Li, Ronan Sicre, Laurent Amsaleg, Michael Backes
In this work, we design good practices to address these limitations, and we present the first comprehensive evaluation of transfer attacks, covering 23 representative attacks against 9 defenses on ImageNet.
1 code implementation • 29 Sep 2022 • Laurent Amsaleg, Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi, Miloš Radovanović, Weeris Treeratanajaru
Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection, similarity search and subspace clustering.
no code implementations • 29 Jun 2022 • Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Finally, to address inconsistencies due to linear target interpolation, we introduce a self-distillation approach to generate and interpolate synthetic targets.
no code implementations • 17 Jun 2022 • Marzieh Gheisari, Javad Amirian, Teddy Furon, Laurent Amsaleg
In some face recognition applications, we are interested to verify whether an individual is a member of a group, without revealing their identity.
no code implementations • 29 Sep 2021 • Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels.
1 code implementation • ICLR 2022 • Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, Laurent Amsaleg, Konstantinos Karantzalos, Yannis Avrithis
In this work, we aim to bridge this gap and improve representations using mixup, which is a powerful data augmentation approach interpolating two or more examples and corresponding target labels at a time.
Ranked #8 on Metric Learning on CUB-200-2011 (using extra training data)
2 code implementations • CVPR 2022 • Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels.
Ranked #1 on Representation Learning on CIFAR10
no code implementations • 24 Feb 2020 • Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member.
no code implementations • 24 Feb 2020 • Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the identity of querying clients.
1 code implementation • 4 Dec 2019 • Hanwei Zhang, Yannis Avrithis, Teddy Furon, Laurent Amsaleg
Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input.
no code implementations • JEPTALNRECITAL 2019 • Antoine Perquin, Gw{\'e}nol{\'e} Lecorv{\'e}, Damien Lolive, Laurent Amsaleg
La qualit{\'e} des plongements est li{\'e}e {\`a} la t{\^a}che sp{\'e}cifique pour laquelle ils ont {\'e}t{\'e} entra{\^\i}n{\'e}s et l{'}{\'e}valuation de cette t{\^a}che peut {\^e}tre un proc{\'e}d{\'e} long et on{\'e}reux s{'}il y a besoin d{'}annotateurs humains.
no code implementations • 23 Apr 2019 • Marzieh Gheisari, Teddy Furon, Laurent Amsaleg
When convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member.
no code implementations • 18 Apr 2019 • Björn Þór Jónsson, Omar Shahbaz Khan, Hanna Ragnarsdóttir, Þórhildur Þorleiksdóttir, Jan Zahálka, Stevan Rudinac, Gylfi Þór Guðmundsson, Laurent Amsaleg, Marcel Worring
Increasing scale is a dominant trend in today's multimedia collections, which especially impacts interactive applications.
1 code implementation • 28 Mar 2019 • Hanwei Zhang, Yannis Avrithis, Teddy Furon, Laurent Amsaleg
This paper investigates the visual quality of the adversarial examples.
no code implementations • 25 May 2018 • Herwig Lejsek, Björn Þór Jónsson, Laurent Amsaleg, Friðrik Heiðar Ásmundsson
Systems designed for visual instance search face the major challenge of scalability: a collection of a few million images used for instance search typically creates a few billion features that must be indexed.
Databases Multimedia