PCDGAN: generation algorithm of adversarial examples based on color perceptual

Lijun Gao; Xuedong Zhang; Jialong Zhu

doi:10.1117/12.3004642

16 August 2023 PCDGAN: generation algorithm of adversarial examples based on color perceptual

Lijun Gao, Xuedong Zhang, Jialong Zhu

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871R (2023) https://doi.org/10.1117/12.3004642
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

Deep neural networks (DNNs) had been proved to be very vulnerable to adversarial examples which generated by adding small perturbation to the input, adversarial examples will mislead DNNs to produce wrong classification results. The quality of adversarial examples should be evaluated from two aspects: attack success rate and visual imperceptibility. The traditional assumption of visual imperceptibility is that perturbation should be tight L2 norm bounds in RGB color space. In the paper, we consider the color perception factors that are more suitable for human eyes on the basis of traditional assumptions. More specifically, we expand the GANs and add color perception factor as a loss function to the training generation model, which made the perturbation more imperceptible. After that, we propose an optimization algorithm of adversarial examples based on Grad-CAM, which is used to optimize the range of perturbation while ensuring the aggressiveness of adversarial examples. Our approach shows that on CIFAR-10 and NLPS 2017 Adversarial Learning Challenge datasets, the adversarial examples created are superior to traditional attack approach in terms of L2 norm and color difference while ensuring attack success rate. The resulting adversarial examples are more natural and usually indistinguishable from the original images.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lijun Gao, Xuedong Zhang, and Jialong Zhu "PCDGAN: generation algorithm of adversarial examples based on color perceptual", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871R (16 August 2023); https://doi.org/10.1117/12.3004642

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KEYWORDS

Color difference

Visualization

Color

RGB color model

Color vision

Education and training

Mathematical optimization

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