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The emerging AI battlespace: Counter-AI threats to AI-powered satellite remote sensing analysis

Bulletin, By Jingjie He | May 13, 2026

mote sensing is a data collection technique that enables the detection and monitoring of physical characteristics of target objects or areas. It is achieved by measuring reflected and emitted radiation from the targets, using optical, radar, light detection and ranging (LiDAR), thermal, multispectral, or hyperspectral sensors deployed on various platforms, including satellites, aircraft, and unmanned aerial vehicles, among others. The acquired data is generally visualized as imagery from an overhead perspective (Campbell, Wynne, and Thomas 2022, 3-23).

Advances in satellite remote sensing and the deployment of satellite constellations have enabled near-persistent Earth observation, which has allowed for significant applications in international security, particularly in arms control and nonproliferation. But challenges remain in processing and analyzing the vast volumes of remote sensing data, primarily due to the reliance on manual analysis by highly trained experts.

Manual analysis faces three key limitations. First, organizations often lack the manpower required to provide comprehensive analytical coverage of remote sensing data. Analyzing satellite imagery requires technical expertise and practical experience, making real-time analysis of large datasets impractical. Second, human analysts may struggle to identify subtle patterns or anomalies, especially in low-resolution images. Even in high-resolution imagery, cognitive biases and target insensitivity may cause analysts to overlook critical information. Third, remote sensing analysis can be serendipitous, with analysts reviewing imagery without a clear sense of what to look for, potentially missing important details.

To address these limitations, researchers have turned to artificial intelligence (AI) and machine learning to analyze satellite imagery at scale. These technologies enable finer granularity, greater accuracy, higher efficiency, and better coverage. But the integration of AI and geospatial science also introduces new challenges, as AI systems can be vulnerable to manipulation through counter-AI techniques.

This article identifies emerging counter-AI threats to satellite imagery analysis and proposes a comprehensive defense framework. It also argues that arms control and nonproliferation missions are not solitary pursuits for seekers but rather dynamic hider-seeker games, where AI functions as both a force and threat multiplier. Adversarial AI attacks—leveraging both digital and physical-world tactics—can be strategically employed to achieve counter-AI objectives, undermining the reliability of AI-driven satellite imagery analyses.

To mitigate these risks, a robust defense framework should encompass five core components: stringent access and quality control for data and models, the integration of robustness into AI frameworks, enhancements to system monitoring capabilities, strengthening cross-sectoral knowledge sharing and threat awareness, and incorporating adaptability and resilience into risk management strategies.

The AI-driven satellite remote sensing revolution

Satellite remote sensing is a powerful tool that can identify objects, detect changes (e.g., facility construction or destruction), and track moving objects (e.g., wartime maneuvers and delivery systems) (Patton et al. 2016). The integration of computer vision, which employs AI to acquire, process, and analyze digital visual data, is revolutionizing the way remote sensing data is interpreted and used.

In particular, AI-driven satellite remote sensing is transforming arms control, nonproliferation, and peacekeeping missions. For example, Amnesty International, in collaboration with Element AI and 28,600 volunteers, developed tools to automatically analyze satellite imagery for monitoring conflicts in Darfur (Cornebise et al. 2018). Palantir Technologies has created MetaConstellation, an AI-powered software for satellite imagery analysis, which has enabled the United States and its allies to automate port monitoring and global submarine deployment tracking (Palantir n.d.). In a joint project with the defense intelligence provider Jane’s, Stanford University, and BlackSky, a satellite imagery provider, the space data analysis company Orbital Insight applied machine learning to assist in the identification of a potential centrifuge assembly facility under construction in Iran (Janes 2021). The US Oak Ridge National Laboratory (2023) also employs AI for applications such as image de-hazing, object counting, and facility function classification. With the precipitous growth of geospatial data, the AI-driven revolution in satellite remote sensing is poised for further acceleration.

Typology of counter-AI attacks

The increasing use of AI-powered satellite remote sensing presents significant security risks. Four primary categories of counter-AI attacks to satellite imagery analysis require attention: data poisoning, model evasion, data inference, and model extraction (see Table 1 below on original).

Data poisoning. Data poisoning attacks aim to contaminate AI models during their training phases by modifying training data. Adversarial artifacts are injected into the data used to train machine learning models, leading to the creation of contaminated models that yield false classifications………………………………………………….

Model Evasion. Model evasion threats are designed to confuse or evade well-trained models by inserting adversarial perturbations—that is, small changes that humans may not be able to perceive—in data that is to be evaluated via machine learning……………………………………………………………………………………..

The potential military applications of adversarial camouflages have attracted considerable interest from researchers. However, the security implications of this technology in satellite remote sensing still require thorough investigation. In addition to evading models designed for satellite imagery analysis, adversarial camouflage could theoretically create decoys, resulting in false alarms that may overwhelm remote sensing systems, particularly when tracking moving objects.

Data Inference. Data inference threats involve attempts to unveil and steal the training data used by an ML model, which can lead to leakage of sensitive information and intelligence……………………………………………………………….

Model Extraction. Model extraction attacks aim at duplicating the functionality of a victim model. In this type of attack, a malicious actor seeks to infer the architecture and parameters of the victim model and subsequently trains a surrogate model using a dataset comprised of inputs and outputs obtained from repeated queries to the victim model. Unlike other types of counter-AI attacks, model extraction specifically targets black-box ML models, whose internal workings are not interpretable by humans…………………………………………………………………….

Prospects for a defense framework

The development of effective countermeasures against counter-AI attacks in satellite imagery analysis is of critical importance. A five-dimensional defense framework could effectively manage and mitigate counter-AI threats…………………………………………………………………………………………………………………………………………………………………………………

Meanwhile, it is crucial to foster a culture of skepticism toward AI. The success of generative AI models such as ChatGPT and DeepSeek has led to the misguided belief that AI understands the world similarly to humans and can make superior and quicker decisions based on logical reasoning. This notion is unfounded: The apparent reasoning of machines is at this point not genuine or reliable; instead, it is based on probabilistic pattern matching derived from extensive training data (Jiang et al., 2024; Mirzadeh et al., 2024; Shi et al., 2023). Consequently, humanizing AI can be detrimental, as it limits our ability to think critically and challenge the models when human judgment diverges from machine conclusions. It is essential to promote knowledge about machine learning’s limitations and to foster a culture of skepticism towards AI………………………………………………………………………………………………………………………………………………………………………………………………. https://thebulletin.org/premium/2026-05/the-emerging-ai-battlespace-counter-ai-threats-to-ai-powered-satellite-remote-sensing-analysis/?utm_source=ActiveCampaign&utm_medium=email&utm_content=The%20emerging%20AI%20battlespace&utm_campaign=20260702%20Thursday%20Newsletter

July 6, 2026 - Posted by Christina Macpherson | technology