PoI+NBU: A Feasibility study in Generating High-Resolution Adversarial Images with a Black Box Evolutional Algorithm based Attack
Adversarial attacks in the digital image domain pose significant challenges to the robustness of machine learning models. Trained convolutional neural networks (CNNs) are among the leading tools used for the automatic classification of images. They are nevertheless exposed to attacks: Given an input...
Saved in:
| Main Authors: | Enea Mancellari, Ali Osman Topal, Franck Leprévost |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universidad San Francisco de Quito USFQ
2025-08-01
|
| Series: | ACI Avances en Ciencias e Ingenierías |
| Subjects: | |
| Online Access: | https://revistas.usfq.edu.ec/index.php/avances/article/view/3699 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
DOG: An Object Detection Adversarial Attack Method
by: Jinpeng Li, et al.
Published: (2025-01-01) -
A comprehensive transplanting of black-box adversarial attacks from multi-class to multi-label models
by: Zhijian Chen, et al.
Published: (2025-03-01) -
Enhancing SAR-ATR Systems’ Resistance to S2M Attacks via FUA: Optimizing Surrogate Models for Adversarial Example Transferability
by: Xiaying Jin, et al.
Published: (2025-01-01) -
Detecting Black-Box Model Probing Attacks Through Probability Scores
by: Yongzhi Wang, et al.
Published: (2025-01-01) -
MAS-PD: Transferable Adversarial Attack Against Vision-Transformers-Based SAR Image Classification Task
by: Boshi Zheng, et al.
Published: (2025-01-01)