TauPad: Test Data Augmentation of Point Clouds by Adversarial Mutation

Point clouds have been widely used in a large number of application scenarios to handle with various deep learning (DL) tasks. Testing is an essential means to guarantee the robustness of DL models, which places high demands on test data. Therefore, it is crucial to design a reliable and effective t...

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Bibliographic Details
Published in:2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) pp. 212 - 216
Main Authors: Liu, Guandi, Liu, Jiawei, Zhang, Quanjun, Fang, Chunrong, Zhang, Xufan
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2022
Subjects:
Data models
Manuals
Measurement
Point cloud compression
Reliability engineering
Robustness
Software and its engineering → Software testing and debugging
Software testing
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Summary:Point clouds have been widely used in a large number of application scenarios to handle with various deep learning (DL) tasks. Testing is an essential means to guarantee the robustness of DL models, which places high demands on test data. Therefore, it is crucial to design a reliable and effective test data augmentation tool of point clouds to generate high-quality data to test the robustness of the target model. However, although common mutation methods can increase the amount of point clouds, the quality of the augmented data still needs to be improved based on the specify of the spatial structure of the point clouds. In this paper, we develop a point clouds augmentation tool, namely TauPad, of which the specific mutation direction is guided by adversarial attacks. Based on the point clouds pre-processing, point clouds adversarial mutation, and spatial distribution restoration, TauPad can generate augmented test data that are significantly deceptive to the target model. Preliminary experiments show that TauPad can reliably and effectively augment point clouds for testing. Its video is at https://youtu.be/Y9nDIEW13_g/ and TauPad can be used at http://1.13.193.98:2600/.
DOI:10.1145/3510454.3517050