LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity

Gubri, Martin; Cordy, Maxime; Papadakis, Mike; Traon, Yves Le; Sen, Koushik

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LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity

Gubri, Martin; Cordy, Maxime; Papadakis, Mike et al.

2022 • In Computer Vision -- ECCV 2022

Peer reviewed

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https://hdl.handle.net/10993/53418

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Keywords :

adversarial machine learning; adversarial examples; deep learning; Transferability; Loss Geometry

Abstract :

[en] We propose transferability from Large Geometric Vicinity (LGV), a new technique to increase the transferability of black-box adversarial attacks. LGV starts from a pretrained surrogate model and collects multiple weight sets from a few additional training epochs with a constant and high learning rate. LGV exploits two geometric properties that we relate to transferability. First, models that belong to a wider weight optimum are better surrogates. Second, we identify a subspace able to generate an effective surrogate ensemble among this wider optimum. Through extensive experiments, we show that LGV alone outperforms all (combinations of) four established test-time transformations by 1.8 to 59.9\% points. Our findings shed new light on the importance of the geometry of the weight space to explain the transferability of adversarial examples.

Disciplines :

Computer science

Author, co-author :

Gubri, Martin ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal

Cordy, Maxime ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal

Papadakis, Mike ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)

Traon, Yves Le

Sen, Koushik

External co-authors :

yes

Language :

English

Title :

LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity

Publication date :

2022

Event name :

EUROPEAN CONFERENCE ON COMPUTER VISION

Event date :

October 23–27, 2022

Audience :

International

Main work title :

Computer Vision -- ECCV 2022

Publisher :

Springer Nature Switzerland

ISBN/EAN :

978-3-031-19772-7

Pages :

603--618

Peer reviewed :

Peer reviewed

Focus Area :

Security, Reliability and Trust

FnR Project :

FNR12669767 - Testing Self-learning Systems, 2018 (01/09/2019-31/08/2022) - Yves Le Traon

Available on ORBilu :

since 30 December 2022

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Bibliography

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