The emerging AI battlespace: Counter-AI threats to AI-powered satellite remote sensing analysis.
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| Title: | The emerging AI battlespace: Counter-AI threats to AI-powered satellite remote sensing analysis. |
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| 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 |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 193710155 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The emerging AI battlespace: Counter-AI threats to AI-powered satellite remote sensing analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22He%2C+Jingjie%22">He, Jingjie</searchLink> (AUTHOR)<i> hejingjie@cass.org.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Bulletin+of+the+Atomic+Scientists%22">Bulletin of the Atomic Scientists</searchLink>. May2026, Vol. 82 Issue 3, p212-220. 9p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Satellite-based+remote+sensing%22">Satellite-based remote sensing</searchLink><br />*<searchLink fieldCode="DE" term="%22Adversarial+machine+learning%22">Adversarial machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+extraction%22">Data extraction</searchLink><br />*<searchLink fieldCode="DE" term="%22Arms+control%22">Arms control</searchLink><br />*<searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=193710155 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00963402.2026.2657746 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 212 Subjects: – SubjectFull: Satellite-based remote sensing Type: general – SubjectFull: Adversarial machine learning Type: general – SubjectFull: Data extraction Type: general – SubjectFull: Arms control Type: general – SubjectFull: Internet security Type: general Titles: – TitleFull: The emerging AI battlespace: Counter-AI threats to AI-powered satellite remote sensing analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: He, Jingjie IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00963402 Numbering: – Type: volume Value: 82 – Type: issue Value: 3 Titles: – TitleFull: Bulletin of the Atomic Scientists Type: main |
| ResultId | 1 |
