The emerging AI battlespace: Counter-AI threats to AI-powered satellite remote sensing analysis.
Saved in:
| Title: | The emerging AI battlespace: Counter-AI threats to AI-powered satellite remote sensing analysis. |
|---|---|
| Authors: | He, Jingjie (AUTHOR) hejingjie@cass.org.cn |
| Source: | Bulletin of the Atomic Scientists. May2026, Vol. 82 Issue 3, p212-220. 9p. |
| Subject Terms: | *Satellite-based remote sensing, *Adversarial machine learning, *Data extraction, *Arms control, *Internet security |
| Abstract: | Satellite remote sensing is increasingly recognized as a critical tool for arms control and nonproliferation missions. Recently, there has been growing interest in leveraging artificial intelligence (AI) and machine learning to enhance analytical efficiency. However, this integration introduces significant risks due to the susceptibility of AI models to counter-AI attacks that may compromise their accuracy, reliability, and security. This article develops a typology of emerging threats posed by counter-AI threats against AI-driven satellite remote sensing, categorizing them in four primary scenarios: data poisoning, model evasion, data inference, and model extraction. These threat vectors span a spectrum of techniques, both digital and physical, raising vital security concerns. To address these risks, the article proposes a comprehensive defense framework centered on five pillars: controlling access and quality of data and models, enhancing the robustness of AI frameworks, advancing system monitoring capabilities, fostering knowledge sharing and threat awareness, and integrating adaptive and resilient risk management. The core message: AI functions not only as a force multiplier but also as a threat multiplier, transforming AI-enabled arms control, nonproliferation, and peacekeeping missions into increasingly dynamic hider—seeker games. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Satellite remote sensing is increasingly recognized as a critical tool for arms control and nonproliferation missions. Recently, there has been growing interest in leveraging artificial intelligence (AI) and machine learning to enhance analytical efficiency. However, this integration introduces significant risks due to the susceptibility of AI models to counter-AI attacks that may compromise their accuracy, reliability, and security. This article develops a typology of emerging threats posed by counter-AI threats against AI-driven satellite remote sensing, categorizing them in four primary scenarios: data poisoning, model evasion, data inference, and model extraction. These threat vectors span a spectrum of techniques, both digital and physical, raising vital security concerns. To address these risks, the article proposes a comprehensive defense framework centered on five pillars: controlling access and quality of data and models, enhancing the robustness of AI frameworks, advancing system monitoring capabilities, fostering knowledge sharing and threat awareness, and integrating adaptive and resilient risk management. The core message: AI functions not only as a force multiplier but also as a threat multiplier, transforming AI-enabled arms control, nonproliferation, and peacekeeping missions into increasingly dynamic hider—seeker games. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 00963402 |
| DOI: | 10.1080/00963402.2026.2657746 |
