ALA: Naturalness-aware Adversarial Lightness Attack
Yihao Huang, Liangru Sun, Qing Guo, Felix Juefei-Xu, Jiayi Zhu, Jincao, Feng, Yang Liu, Geguang Pu

TL;DR
This paper introduces ALA, a naturalness-aware adversarial attack modifying image lightness to fool models while maintaining perceptual naturalness, improving robustness and practicality over traditional methods.
Contribution
The paper proposes a novel lightness-based adversarial attack with naturalness-aware regularization, enhancing attack success and realism compared to existing methods.
Findings
Effective on ImageNet and Places-365 datasets
Maintains natural appearance of adversarial images
Achieves high attack success rate
Abstract
Most researchers have tried to enhance the robustness of DNNs by revealing and repairing the vulnerability of DNNs with specialized adversarial examples. Parts of the attack examples have imperceptible perturbations restricted by Lp norm. However, due to their high-frequency property, the adversarial examples can be defended by denoising methods and are hard to realize in the physical world. To avoid the defects, some works have proposed unrestricted attacks to gain better robustness and practicality. It is disappointing that these examples usually look unnatural and can alert the guards. In this paper, we propose Adversarial Lightness Attack (ALA), a white-box unrestricted adversarial attack that focuses on modifying the lightness of the images. The shape and color of the samples, which are crucial to human perception, are barely influenced. To obtain adversarial examples with a high…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Advanced Neural Network Applications · Anomaly Detection Techniques and Applications
