On GAN-based Data Integrity Attacks Against Robotic Spatial Sensing
Communication is arguably the most important way to enable cooperation among multiple robots. In numerous such settings, robots exchange local sensor measurements to form a global perception of the environment. One example of this setting is adaptive multi-robot informative path planning, where robo...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2024-05-01
|
| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135554 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849762729382903808 |
|---|---|
| author | Tamim Khatib Patrick Kreidl Ayan Dutta Ladislau Boloni Swapnoneel Roy |
| author_facet | Tamim Khatib Patrick Kreidl Ayan Dutta Ladislau Boloni Swapnoneel Roy |
| author_sort | Tamim Khatib |
| collection | DOAJ |
| description | Communication is arguably the most important way to enable cooperation among multiple robots. In numerous such settings, robots exchange local sensor measurements to form a global perception of the environment. One example of this setting is adaptive multi-robot informative path planning, where robots’ local measurements are “fused” using probabilistic techniques (e.g., Gaussian process models) for more accurate prediction of the underlying ambient phenomena. In an adversarial setting, in which we assume a malicious entity–-the adversary-–can modify data exchanged during inter-robot communications, these cooperating robots become vulnerable to data integrity attacks. Such attacks on a multi-robot informative path planning system may, for example, replace the original sensor measurements with fake measurements to negatively affect achievable prediction accuracy. In this paper, we study how such an adversary may design data integrity attacks using a Generative Adversarial Network
(GAN). Results show the GAN-based techniques learning spatial patterns in training data to produce fake measurements that are relatively undetectable yet significantly degrade prediction accuracy. |
| format | Article |
| id | doaj-art-dc65cafb664d48c78b05ed9a15e6b09d |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-dc65cafb664d48c78b05ed9a15e6b09d2025-08-20T03:05:39ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13555471933On GAN-based Data Integrity Attacks Against Robotic Spatial SensingTamim KhatibPatrick KreidlAyan DuttaLadislau BoloniSwapnoneel RoyCommunication is arguably the most important way to enable cooperation among multiple robots. In numerous such settings, robots exchange local sensor measurements to form a global perception of the environment. One example of this setting is adaptive multi-robot informative path planning, where robots’ local measurements are “fused” using probabilistic techniques (e.g., Gaussian process models) for more accurate prediction of the underlying ambient phenomena. In an adversarial setting, in which we assume a malicious entity–-the adversary-–can modify data exchanged during inter-robot communications, these cooperating robots become vulnerable to data integrity attacks. Such attacks on a multi-robot informative path planning system may, for example, replace the original sensor measurements with fake measurements to negatively affect achievable prediction accuracy. In this paper, we study how such an adversary may design data integrity attacks using a Generative Adversarial Network (GAN). Results show the GAN-based techniques learning spatial patterns in training data to produce fake measurements that are relatively undetectable yet significantly degrade prediction accuracy.https://journals.flvc.org/FLAIRS/article/view/135554multi-robot systemsdata integrity attacksgenerative adversarial network |
| spellingShingle | Tamim Khatib Patrick Kreidl Ayan Dutta Ladislau Boloni Swapnoneel Roy On GAN-based Data Integrity Attacks Against Robotic Spatial Sensing Proceedings of the International Florida Artificial Intelligence Research Society Conference multi-robot systems data integrity attacks generative adversarial network |
| title | On GAN-based Data Integrity Attacks Against Robotic Spatial Sensing |
| title_full | On GAN-based Data Integrity Attacks Against Robotic Spatial Sensing |
| title_fullStr | On GAN-based Data Integrity Attacks Against Robotic Spatial Sensing |
| title_full_unstemmed | On GAN-based Data Integrity Attacks Against Robotic Spatial Sensing |
| title_short | On GAN-based Data Integrity Attacks Against Robotic Spatial Sensing |
| title_sort | on gan based data integrity attacks against robotic spatial sensing |
| topic | multi-robot systems data integrity attacks generative adversarial network |
| url | https://journals.flvc.org/FLAIRS/article/view/135554 |
| work_keys_str_mv | AT tamimkhatib onganbaseddataintegrityattacksagainstroboticspatialsensing AT patrickkreidl onganbaseddataintegrityattacksagainstroboticspatialsensing AT ayandutta onganbaseddataintegrityattacksagainstroboticspatialsensing AT ladislauboloni onganbaseddataintegrityattacksagainstroboticspatialsensing AT swapnoneelroy onganbaseddataintegrityattacksagainstroboticspatialsensing |