Adversarial Examples by Perturbing High-level Features in Intermediate Decoder Layers
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359626" target="_blank" >RIV/68407700:21230/22:00359626 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0010892800003116" target="_blank" >https://doi.org/10.5220/0010892800003116</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010892800003116" target="_blank" >10.5220/0010892800003116</a>
Alternative languages
Result language
angličtina
Original language name
Adversarial Examples by Perturbing High-level Features in Intermediate Decoder Layers
Original language description
We propose a novel method for creating adversarial examples. Instead of perturbing pixels, we use an encoder-decoder representation of the input image and perturb intermediate layers in the decoder. This changes the high-level features provided by the generative model. Therefore, our perturbation possesses semantic meaning, such as a longer beak or green tints. We formulate this task as an optimization problem by minimizing the Wasserstein distance between the adversarial and initial images under a misclassification constraint. We employ the projected gradient method with a simple inexact projection. Due to the projection, all iterations are feasible, and our method always generates adversarial images. We perform numerical experiments by fooling MNIST and ImageNet classifiers in both targeted and untargeted settings. We demonstrate that our adversarial images are much less vulnerable to steganographic defence techniques than pixel-based attacks. Moreover, we show that our method modifies key features such as edges and that defence techniques based on adversarial training are vulnerable to our attacks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2
ISBN
978-989-758-547-0
ISSN
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e-ISSN
2184-433X
Number of pages
12
Pages from-to
496-507
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Porto
Event location
Online Streaming
Event date
Mar 3, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000774441800046