Improving Tiled Evolutionary Adversarial Attack
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10493456" target="_blank" >RIV/00216208:11320/24:10493456 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-74627-7_40" target="_blank" >https://doi.org/10.1007/978-3-031-74627-7_40</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-74627-7_40" target="_blank" >10.1007/978-3-031-74627-7_40</a>
Alternative languages
Result language
angličtina
Original language name
Improving Tiled Evolutionary Adversarial Attack
Original language description
Adversarial examples are a well-known phenomenon in image classification. They represent maliciously altered inputs that a deep learning model classifies incorrectly, even though the added noise is almost indistinguishable to the human eye. Defense against adversarial examples can be either proactive or reactive. This paper builds upon previous work, which tests one of the state-of-the-art reactive defenses. While the previous work managed to defeat the defense using an evolutionary attack, a notable drawback was the visible adversarial noise. This work improves this by utilizing the Structural Similarity Index (SSIM) for measuring the distance between benign and adversarial inputs, and by implementing a new mutation during the evolution process. These adjustments not only created adversarial images with less visible noise, but also accelerated the process of generating them.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
ISBN
978-3-031-74627-7
ISSN
1865-0937
e-ISSN
1865-0937
Number of pages
11
Pages from-to
480-490
Publisher name
Springer
Place of publication
Cham
Event location
Turin, Italy
Event date
Sep 18, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—