Adversarial Attacks on 3D Target Recognition Driven by Gradient Adaptive Adjustment

LIU Weiquan; SHEN Xiaoying; LIU Dunqiang; SUN Yanwen; CAI Guorong; ZANG Yu; SHEN Siqi; WANG Cheng

doi:10.11999/JEIT251264

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LIU Weiquan, SHEN Xiaoying, LIU Dunqiang, SUN Yanwen, CAI Guorong, ZANG Yu, SHEN Siqi, WANG Cheng. Adversarial Attacks on 3D Target Recognition Driven by Gradient Adaptive Adjustment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251264

Citation:

LIU Weiquan, SHEN Xiaoying, LIU Dunqiang, SUN Yanwen, CAI Guorong, ZANG Yu, SHEN Siqi, WANG Cheng. Adversarial Attacks on 3D Target Recognition Driven by Gradient Adaptive Adjustment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251264

Citation:

LIU Weiquan, SHEN Xiaoying, LIU Dunqiang, SUN Yanwen, CAI Guorong, ZANG Yu, SHEN Siqi, WANG Cheng. Adversarial Attacks on 3D Target Recognition Driven by Gradient Adaptive Adjustment[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251264

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Adversarial Attacks on 3D Target Recognition Driven by Gradient Adaptive Adjustment

doi: 10.11999/JEIT251264 cstr: 32379.14.JEIT251264

1.
College of Computer Engineering, Jimei University, Xiamen 361021, China
2.
School of Informatics, Xiamen University, Xiamen 361005, China

Funds: The National Natural Science Foundation of China (62401225, 62471415), The Fujian Provincial Natural Science Foundation of China (2025J0141, 2024J01115, 2023J01004), The Xiamen Natural Science Foundation of China (3502Z202472018)

Received Date: 2025-11-13
Accepted Date: 2026-01-04
Rev Recd Date: 2026-01-04

Available Online: 2026-01-15

Abstract

Abstract

In recent years, the deep integration of artificial intelligence and optoelectronic perception systems has significantly propelled the advancement of intelligent driving technologies, with LiDAR serving as a core sensing modality that acquires high-precision, high-resolution three-dimensional point cloud data, thereby establishing itself as an indispensable information source for environmental perception in intelligent driving systems. However, deep learning-based 3D point cloud recognition models exhibit marked vulnerability to meticulously crafted adversarial perturbations, leading to a sharp degradation in recognition performance and posing a serious security challenge to these optoelectronic perception systems. Research on adversarial attack methods for 3D point clouds is therefore crucial not only for enhancing model robustness but also for ensuring the safe and reliable operation of intelligent driving systems. While existing attack methods have improved in effectiveness, their generated perturbations often lack concealment, produce outliers, and demonstrate poor imperceptibility, limiting their practical application in real-world scenarios. To address these issues, this paper proposes a Gradient Adaptive Adjustment (GAA) driven point cloud adversarial attack method. This approach begins by analyzing the decision-level vulnerabilities of 3D point cloud classifiers to identify key points significantly influencing the model’s output. It then adaptively adjusts gradient weights by incorporating local curvature information and optimizes perturbation generation under geometric constraints aligned with principal curvature directions, thereby ensuring a high attack success rate while maintaining the geometric consistency and visual naturalness of the adversarial point cloud. Experimental results on multiple public datasets demonstrate that the proposed method achieves a high attack success rate while significantly reducing perturbation intensity; for instance, on the ModelNet40 dataset against the PointNet model, it attains a 97.69% attack success rate by perturbing only 28 points on average, substantially outperforming existing comparative methods and providing an effective tool for evaluating and enhancing the security of intelligent driving optoelectronic perception systems.
- 3D point cloud,
- target recognition,
- adversarial attack

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References(28)

References

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