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Laurent Amsaleg - IEEE Xplore Author Profile
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Laurent Amsaleg

Also published under: L. Amsaleg

Publications
28
Citations
593
Publications by Year
19922022
Co-Authors:
Show All Co-Authors (51)
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Laurent Amsaleg

Also published under: L. Amsaleg

Affiliation

Inria
Univ Rennes
CNRS, IRISA

Publication Topics

Biography

Laurent Amsaleg (Member, IEEE) received the Ph.D. degree from the University of Paris 6. He is currently a Senior Researcher with CNRS. He also leads the Linkmedia Research Group, IRISA/INRIA Laboratory, Rennes, France. His research interests include high-dimensional indexing at scale and multimedia analytics, as well as the many facets of the security issues in relation with the processing of extremely large collections of multimedia material. Topics dealing with privacy and adversarial machine learning are therefore central to his work.(Based on document published on 10 September 2020).
Publications
28
Citations
593
Publications by Year
19922022
Co-Authors:
Show All Co-Authors (51)
Author's Published Works

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AlignMixup: Improving Representations By Interpolating Aligned Features

Shashanka Venkataramanan;Ewa Kijak;Laurent Amsaleg;Yannis Avrithis

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (40)
Mixup is a powerful data augmentation method that in-terpolates between two or more examples in the input or feature space and between the corresponding target labels. However, how to best interpolate images is not well defined. Recent mixup methods overlay or cut-and-paste two or more objects into one image, which needs care in selecting regions. Mixup has also been connected to autoencoders, bec...Show More

AlignMixup: Improving Representations By Interpolating Aligned Features

Shashanka Venkataramanan;Ewa Kijak;Laurent Amsaleg;Yannis Avrithis

Year: 2022 | Conference Paper |

High Intrinsic Dimensionality Facilitates Adversarial Attack: Theoretical Evidence

Laurent Amsaleg;James Bailey;Amélie Barbe;Sarah M. Erfani;Teddy Furon;Michael E. Houle;Miloš Radovanović;Xuan Vinh Nguyen

IEEE Transactions on Information Forensics and Security
Year: 2021 | Volume: 16 | Journal Article |
Cited by: Papers (20)
Machine learning systems are vulnerable to adversarial attack. By applying to the input object a small, carefully-designed perturbation, a classifier can be tricked into making an incorrect prediction. This phenomenon has drawn wide interest, with many attempts made to explain it. However, a complete understanding is yet to emerge. In this paper we adopt a slightly different perspective, still rel...Show More

High Intrinsic Dimensionality Facilitates Adversarial Attack: Theoretical Evidence

Laurent Amsaleg;James Bailey;Amélie Barbe;Sarah M. Erfani;Teddy Furon;Michael E. Houle;Miloš Radovanović;Xuan Vinh Nguyen

Year: 2021 | Volume: 16 | Journal Article |

Walking on the Edge: Fast, Low-Distortion Adversarial Examples

Hanwei Zhang;Yannis Avrithis;Teddy Furon;Laurent Amsaleg

IEEE Transactions on Information Forensics and Security
Year: 2021 | Volume: 16 | Journal Article |
Cited by: Papers (37)
Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural given the increasing applications of deep neural networks in our everyday lives. When white-box attacks are almost always successful, it is typically only the distortion of the perturbations that matters in their evalua...Show More

Walking on the Edge: Fast, Low-Distortion Adversarial Examples

Hanwei Zhang;Yannis Avrithis;Teddy Furon;Laurent Amsaleg

Year: 2021 | Volume: 16 | Journal Article |

Joint Learning of Assignment and Representation for Biometric Group Membership

Marzieh Gheisari;Teddy Furon;Laurent Amsaleg

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
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. This framework learns the embedding parameters, group representations and assignments simultaneously. Experiments show the trade-off between security/privacy and verification/identification perf...Show More

Joint Learning of Assignment and Representation for Biometric Group Membership

Marzieh Gheisari;Teddy Furon;Laurent Amsaleg

Year: 2020 | Conference Paper |

Privacy Preserving Group Membership Verification and Identification

Marzieh Gheisari;Teddy Furon;Laurent Amsaleg

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
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. Similarly, group membership identification states which group the individual belongs to, without knowing his/her identity. A recent contribution provides privacy and security for group membership protocols through the joint use of two...Show More

Privacy Preserving Group Membership Verification and Identification

Marzieh Gheisari;Teddy Furon;Laurent Amsaleg

Year: 2019 | Conference Paper |

Group Membership Verification with Privacy: Sparse or Dense?

Marzieh Gheisari;Teddy Furon;Laurent Amsaleg

2019 IEEE International Workshop on Information Forensics and Security (WIFS)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy for group membership protocols through the joint use of two mechanisms: quantizing templates into discrete embeddings, and aggregating several templates into one group representation.However, this scheme has one drawback:...Show More

Group Membership Verification with Privacy: Sparse or Dense?

Marzieh Gheisari;Teddy Furon;Laurent Amsaleg

Year: 2019 | Conference Paper |

Aggregation and Embedding for Group Membership Verification

Marzieh Gheisari;Teddy Furon;Laurent Amsaleg;Behrooz Razeghi;Slava Voloshynovskiy

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2019 | Conference Paper |
Cited by: Papers (7)
This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also aggregates multiple embeddings into representative values, impeding identification. Theoretical and e...Show More

Aggregation and Embedding for Group Membership Verification

Marzieh Gheisari;Teddy Furon;Laurent Amsaleg;Behrooz Razeghi;Slava Voloshynovskiy

Year: 2019 | Conference Paper |

The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality

Laurent Amsaleg;James Bailey;Dominique Barbe;Sarah Erfani;Michael E. Houle;Vinh Nguyen;Miloš Radovanović

2017 IEEE Workshop on Information Forensics and Security (WIFS)
Year: 2017 | Conference Paper |
Cited by: Papers (21)
Recent research has shown that machine learning systems, including state-of-the-art deep neural networks, are vulnerable to adversarial attacks. By adding to the input object an imperceptible amount of adversarial noise, it is highly likely that the classifier can be tricked into assigning the modified object to any desired class. It has also been observed that these adversarial samples generalize...Show More

The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality

Laurent Amsaleg;James Bailey;Dominique Barbe;Sarah Erfani;Michael E. Houle;Vinh Nguyen;Miloš Radovanović

Year: 2017 | Conference Paper |

Privacy-Preserving Outsourced Media Search

Li Weng;Laurent Amsaleg;Teddy Furon

IEEE Transactions on Knowledge and Data Engineering
Year: 2016 | Volume: 28, Issue: 10 | Journal Article |
Cited by: Papers (36)
This work proposes a privacy-protection framework for an important application called outsourced media search. This scenario involves a data owner, a client, and an untrusted server, where the owner outsources a search service to the server. Due to lack of trust, the privacy of the client and the owner should be protected. The framework relies on multimedia hashing and symmetric encryption. It req...Show More

Privacy-Preserving Outsourced Media Search

Li Weng;Laurent Amsaleg;Teddy Furon

Year: 2016 | Volume: 28, Issue: 10 | Journal Article |

Near-duplicate video detection based on an approximate similarity self-join strategy

Henrique B. da Silva;Zenilton K. G. do Patrocínio;Guillaume Gravier;Laurent Amsaleg;Arnaldo de A. Araújo;Silvio Jamil F. Guimarães

2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)
Year: 2016 | Conference Paper |
Cited by: Papers (1)
The huge amount of redundant multimedia data, like video, has become a problem in terms of both space and copyright. Usually, the methods for identifying near-duplicate videos are neither adequate nor scalable to find pairs of similar videos. Similarity self-join operation could be an alternative to solve this problem in which all similar pairs of elements from a video dataset are retrieved. Nonet...Show More

Near-duplicate video detection based on an approximate similarity self-join strategy

Henrique B. da Silva;Zenilton K. G. do Patrocínio;Guillaume Gravier;Laurent Amsaleg;Arnaldo de A. Araújo;Silvio Jamil F. Guimarães

Year: 2016 | Conference Paper |

A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval

Li Weng;Laurent Amsaleg;April Morton;Stéphane Marchand-Maillet

IEEE Transactions on Information Forensics and Security
Year: 2015 | Volume: 10, Issue: 1 | Journal Article |
Cited by: Papers (76)
We propose a privacy protection framework for large-scale content-based information retrieval. It offers two layers of protection. First, robust hash values are used as queries to prevent revealing original content or features. Second, the client can choose to omit certain bits in a hash value to further increase the ambiguity for the server. Due to the reduced information, it is computationally d...Show More

A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval

Li Weng;Laurent Amsaleg;April Morton;Stéphane Marchand-Maillet

Year: 2015 | Volume: 10, Issue: 1 | Journal Article |

Image retrieval with reciprocal and shared nearest neighbors

Agni Delvinioti;Hervé Jégou;Laurent Amsaleg;Michael E. Houle

2014 International Conference on Computer Vision Theory and Applications (VISAPP)
Year: 2014 | Conference Paper |
Content-based image retrieval systems typically rely on a similarity measure between image vector representations, such as in bag-of-words, to rank the database images in decreasing order of expected relevance to the query. However, the inherent asymmetry of k-nearest neighborhoods is not properly accounted for by traditional similarity measures, possibly leading to a loss of retrieval accuracy. T...Show More

Image retrieval with reciprocal and shared nearest neighbors

Agni Delvinioti;Hervé Jégou;Laurent Amsaleg;Michael E. Houle

Year: 2014 | Conference Paper |

Fast and secure similarity search in high dimensional space

Teddy Furon;Hervé Jégou;Laurent Amsaleg;Benjamin Mathon

2013 IEEE International Workshop on Information Forensics and Security (WIFS)
Year: 2013 | Conference Paper |
Cited by: Papers (6)
Similarity search in high dimensional space database is split into two worlds: i) fast, scalable, and approximate search algorithms which are not secure, and ii) search protocols based on secure computation which are not scalable. This paper presents a one-way privacy protocol that lies in between these two worlds. Approximate metrics for the cosine similarity allows speed. Elements of large rando...Show More

Fast and secure similarity search in high dimensional space

Teddy Furon;Hervé Jégou;Laurent Amsaleg;Benjamin Mathon

Year: 2013 | Conference Paper |

Terabyte-scale image similarity search: Experience and best practice

Diana Moise;Denis Shestakov;Gylfi Gudmundsson;Laurent Amsaleg

2013 IEEE International Conference on Big Data
Year: 2013 | Conference Paper |
Cited by: Papers (17)
While the past decade has witnessed an unprecedented growth of data generated and collected all over the world, existing data management approaches lack the ability to address the challenges of Big Data. One of the most promising tools for Big Data processing is the MapReduce paradigm. Although it has its limitations, the MapReduce programming model has laid the foundations for answering some of t...Show More

Terabyte-scale image similarity search: Experience and best practice

Diana Moise;Denis Shestakov;Gylfi Gudmundsson;Laurent Amsaleg

Year: 2013 | Conference Paper |

Scalable high-dimensional indexing with Hadoop

Denis Shestakov;Diana Moise;Gylfi Gudmundsson;Laurent Amsaleg

2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)
Year: 2013 | Conference Paper |
Cited by: Papers (4)
While high-dimensional search-by-similarity techniques reached their maturity and in overall provide good performance, most of them are unable to cope with very large multimedia collections. The `big data' challenge however has to be addressed as multimedia collections have been explosively growing and will grow even faster than ever within the next few years. Luckily, computational processing pow...Show More

Scalable high-dimensional indexing with Hadoop

Denis Shestakov;Diana Moise;Gylfi Gudmundsson;Laurent Amsaleg

Year: 2013 | Conference Paper |

Enlarging hacker's toolbox: Deluding image recognition by attacking keypoint orientations

Thanh-Toan Do;Ewa Kijak;Laurent Amsaleg;Teddy Furon

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2012 | Conference Paper |
Cited by: Papers (9)
Content-Based Image Retrieval Systems (CBIRS) used in forensics related contexts require very good image recognition capabilities. Whereas the robustness criterion has been extensively covered by Computer Vision or Multimedia literature, none of these communities explored the security of CBIRS. Recently, preliminary studies have shown real systems can be deluded by applying transformations to imag...Show More

Enlarging hacker's toolbox: Deluding image recognition by attacking keypoint orientations

Thanh-Toan Do;Ewa Kijak;Laurent Amsaleg;Teddy Furon

Year: 2012 | Conference Paper |

Distributed high-dimensional index creation using Hadoop, HDFS and C++

Gylfi Þór Gudmundsson;Laurent Amsaleg;Björn Þór Jónsson

2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)
Year: 2012 | Conference Paper |
Cited by: Papers (3)
This paper describes an initial study where the open-source Hadoop parallel and distributed run-time environment is used to speedup the construction phase of a large high-dimensional index. This paper first discusses the typical practical problems developers may run into when porting their code to Hadoop. It then presents early experimental results showing that the performance gains are substantia...Show More

Distributed high-dimensional index creation using Hadoop, HDFS and C++

Gylfi Þór Gudmundsson;Laurent Amsaleg;Björn Þór Jónsson

Year: 2012 | Conference Paper |

Using PhotoCube as an extensible demonstration platform for advanced image analysis techniques

Grímur Tómasson;Gísli Kristján Ólafsson;Hlynur SigurÞórsson;Björn Þór Jónsson;Kristján Rúnarsson;Laurent Amsaleg

2012 10th International Workshop on Content-Based Multimedia Indexing (CBMI)
Year: 2012 | Conference Paper |
Cited by: Papers (1)
As digital image collections have been growing ever larger, the multimedia community has put emphasis on methods for image content analysis and presentation. To facilitate extensive user studies of these methods, a single platform is needed that can uniformly incorporate all the analysis and presentation methods under study. Due to its extensibility features, a plug-in API for image analysis metho...Show More

Using PhotoCube as an extensible demonstration platform for advanced image analysis techniques

Grímur Tómasson;Gísli Kristján Ólafsson;Hlynur SigurÞórsson;Björn Þór Jónsson;Kristján Rúnarsson;Laurent Amsaleg

Year: 2012 | Conference Paper |

Balancing clusters to reduce response time variability in large scale image search

Romain Tavenard;Hervé Jégou;Laurent Amsaleg

2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI)
Year: 2011 | Conference Paper |
Cited by: Papers (11)
Many algorithms for approximate nearest neighbor search in high-dimensional spaces partition the data into clusters. At query time, for efficiency, an index selects the few (or a single) clusters nearest to the query point. Clusters are often produced by the well-known k-means approach since it has several desirable properties. On the downside, it tends to produce clusters having quite different c...Show More

Balancing clusters to reduce response time variability in large scale image search

Romain Tavenard;Hervé Jégou;Laurent Amsaleg

Year: 2011 | Conference Paper |

Searching in one billion vectors: Re-rank with source coding

Hervé Jégou;Romain Tavenard;Matthijs Douze;Laurent Amsaleg

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2011 | Conference Paper |
Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification scheme, which performs exact distance calculation on the short-list of hypotheses, the est...Show More

Searching in one billion vectors: Re-rank with source coding

Hervé Jégou;Romain Tavenard;Matthijs Douze;Laurent Amsaleg

Year: 2011 | Conference Paper |

Challenging the security of Content-Based Image Retrieval systems

Thanh-Toan Do;Ewa Kijak;Teddy Furon;Laurent Amsaleg

2010 IEEE International Workshop on Multimedia Signal Processing
Year: 2010 | Conference Paper |
Cited by: Papers (5)
Content-Based Image Retrieval (CBIR) has been recently used as a filtering mechanism against the piracy of multimedia contents. Many publications in the last few years have proposed very robust schemes where pirated contents are detected despite severe modifications. As none of these systems have addressed the piracy problem from a security perspective, it is time to check whether they are secure:...Show More

Challenging the security of Content-Based Image Retrieval systems

Thanh-Toan Do;Ewa Kijak;Teddy Furon;Laurent Amsaleg

Year: 2010 | Conference Paper |

NV-Tree: An Efficient Disk-Based Index for Approximate Search in Very Large High-Dimensional Collections

Herwig Lejsek;Friðrik Heiðar Ásmundsson;Björn Þór Jónsson;Laurent Amsaleg

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2009 | Volume: 31, Issue: 5 | Journal Article |
Cited by: Papers (56)
Over the last two decades, much research effort has been spent on nearest neighbor search in high-dimensional data sets. Most of the approaches published thus far have, however, only been tested on rather small collections. When large collections have been considered, high-performance environments have been used, in particular systems with a large main memory. Accessing data on disk has largely be...Show More

NV-Tree: An Efficient Disk-Based Index for Approximate Search in Very Large High-Dimensional Collections

Herwig Lejsek;Friðrik Heiðar Ásmundsson;Björn Þór Jónsson;Laurent Amsaleg

Year: 2009 | Volume: 31, Issue: 5 | Journal Article |

Dynamic behavior of balanced NV-trees

Arnar Olafsson;Bjorn Por Jonsson;Laurent Amsaleg

2008 International Workshop on Content-Based Multimedia Indexing
Year: 2008 | Conference Paper |
Cited by: Papers (5)
Recently, some approximate high-dimensional indexing techniques have shown promising results by trading off quality guarantees for improved query performance. While the query performance of these methods has been well studied, however, the performance of index maintenance has not yet been reported in any detail. In this paper we focus on the dynamic behavior of the NV-tree, which is a disk-based a...Show More

Dynamic behavior of balanced NV-trees

Arnar Olafsson;Bjorn Por Jonsson;Laurent Amsaleg

Year: 2008 | Conference Paper |

Query adaptative locality sensitive hashing

Herve Jegou;Laurent Amsaleg;Cordelia Schmid;Patrick Gros

2008 IEEE International Conference on Acoustics, Speech and Signal Processing
Year: 2008 | Conference Paper |
It is well known that high-dimensional nearest-neighbor retrieval is very expensive. Many signal processing methods suffer from this computing cost. Dramatic performance gains can be obtained by using approximate search, such as the popular Locality-Sensitive Hashing. This paper improves LSH by performing an on-line selection of the most appropriate hash functions from a pool of functions. An addi...Show More

Query adaptative locality sensitive hashing

Herve Jegou;Laurent Amsaleg;Cordelia Schmid;Patrick Gros

Year: 2008 | Conference Paper |

The Eff2 Project: Towards Efficient and Effective Support for Large-Scale High-Dimensional Indexing

Bjorn Por Jonsson;Herwig Lejsek;Laurent Amsaleg

2007 International Workshop on Content-Based Multimedia Indexing
Year: 2007 | Conference Paper |
Research in content-based multimedia retrieval needs to be a joint venture between the multimedia processing and database communities, in order to be usable in practice at a realistic scale. Unfortunately, however, while both communities have performed some excellent research, they have done so in isolation from each other. In this paper we describe the lessons learned from the Eff2 project on Eff...Show More

The Eff2 Project: Towards Efficient and Effective Support for Large-Scale High-Dimensional Indexing

Bjorn Por Jonsson;Herwig Lejsek;Laurent Amsaleg

Year: 2007 | Conference Paper |

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