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Adversarial attacks dataset for low light image enhancement.

Axel Martinez¹, Matthieu Olague², Gustavo Olague¹

¹Department of Computer Science, CICESE, Ensenada, 22860, México.

|June 12, 2025

Summary

This summary is machine-generated.

This study introduces a new dataset for evaluating low-light image enhancement algorithms against adversarial attacks. The dataset helps improve the reliability of deep learning models in critical applications like autonomous driving and surveillance.

Keywords:

Adversarial examples Adversarial robustness Deep learning Symbolic learning

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Low-light image enhancement is crucial for applications like surveillance and autonomous driving.
Deep convolutional neural networks (CNNs) are effective but vulnerable to adversarial attacks, posing reliability challenges.

Purpose of the Study:

To create a comprehensive dataset for evaluating the adversarial robustness of low-light image enhancement algorithms.
To provide a resource for researchers to test and improve the security of image enhancement systems.

Main Methods:

Developed a dataset of 4970 digital images by applying 12 types of adversarial attacks to five established image databases (MEF, LIME, LOLv1, LOLv2-R, LOLv2-S).
Included original images alongside adversarial examples for direct comparison and analysis.
The dataset covers diverse scenarios including landscapes, objects, and locations under varying illumination conditions.

Main Results:

The repository offers a standardized benchmark for assessing adversarial robustness in low-light image enhancement.
Facilitates the study of vulnerabilities and the development of more resilient deep learning models.

Conclusions:

The proposed dataset is essential for advancing research in robust low-light image enhancement.
Addressing adversarial vulnerabilities is critical for deploying these technologies reliably in real-world applications.