Why camouflage designers can’t ignore AI

Modern camouflage nets are designed to disappear into their surroundings, but they’re also designed for mass production. Rather than the patterns on them being entirely randomised, they repeat every so often in accordance with the printing template being used. This ensures consistency and quality – but it could also make them easier to discover as artificial intelligence begins to play an increasing role in target recognition.

Leading camouflage researcher Dr Alexander Schwegmann has warned that AI’s advanced ability to spot patterns in sensor data is swinging the camouflage-sensor arms race back in favour of detection systems. He has urged camouflage designers to factor in the impact of AI when designing camouflage and to use AI assistance when creating new solutions.   

“AI learns recurring features, and camouflage solutions [that are] too uniform tend to make classification easier,” he says. 

“This means that while camouflage systems still need to hide things from humans, they now need to take a dual approach and also deceive AI.” 

Multiple AI applications 

Dr Schwegmann, who works at Germany’s Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB), has published extensively on camouflage assessment and multi-spectral signature management. He explains that the same AI surge that has reshaped fields from medicine to agriculture is now rapidly transforming asset detection in the battlefield.

Dr Alexander Schwegmann, Germany’s Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB).

Discriminative AI, which recognises and classifies patterns, can significantly enhance the sorting and analysis of data gathered from sensors in a range of ways. It is being used, for example, in data fusion, where AI combines inputs from multiple sensors – such as visual, thermal and radar – into a coherent picture. It also supports pattern recognition, identifying features associated with potential targets and flagging objects of interest within vast data streams. These outputs can then be fed into decision-support systems, helping analysts to prioritise what to investigate and accelerating the conversion of raw data into actionable information. Finally, AI enables ongoing tracking, continuously monitoring identified objects across time and sensor feeds to maintain situational awareness. 

“AI is most powerful wherever very large volumes of sensor data have to be fused, pre-processed, and prioritized quickly,” explains Dr Schwegmann. “In practice, that means automatic target recognition in images and video streams, faster pattern recognition, and a significant reduction of the human workload in screening large [intelligence, surveillance and reconnaissance] data volumes. The main gain is not simply more enhanced detection, but faster conversion of raw sensor data into information that we can actually use or that can actually support assessment in decision making.”

Strengths and weaknesses of AI 

Dr Schwegmann explains light, affordable sensors fitted on platforms such as rotary wing drones have led to an explosion in battlefield data in recent years. While in the past, the sheer volume of such information could never have been  analysed in a time-effective ways by humans, data from multiple sensors types and locations can now be fused into a single coherent picture. At the same time, discriminative AI models are being trained with countless images, as well as infrared signatures, radar returns and other multi-spectral sensor data. This enables them to rapidly identify deployed assets, like tanks, supply facilities or artillery battalions, for targeting and destruction. 

Human beings in the target recognition loop can then review and validate AI-generated outputs before decisions are made. 
While the development should be alarming for any nation deploying armed forces, Dr Schwegmann says for the moment there are still limitations on AI’s abilities. “AI systems struggle with heavily concealed, partially occluded  objects that are low contrast, or where the context is not the right one,” he says. “They also tend to struggle when viewing conditions differ too much from the data that were trained on. So, AI is strongest on what it has seen often – and when reality differs from this, camouflage, clutter and real-world variability still matter.” 

Dr Schwegmann says while AI is swinging the balance toward target detection, there is plenty camouflage manufacturers can do to fight back. The key is designing systems that take into account not just conventional sensors operated  by humans, but AI-enhanced systems. One approach may be to look at increased variation in camouflage patterns and scale. The less uniformity and consistency present in camouflage solutions, the lower the chance that AI will spot a pattern and deduce that an asset is being hidden. Camouflage must now also be designed and tested across multiple spectral bands simultaneously, as a solution that appears effective visually may still stand out to AI enhanced sensors operating in thermal or radar domains. 

Growing importance of  ‘adversarial camouflage’ 

Another important avenue to explore is adversarial camouflage. This involves embedding camouflage netting and other products with patterns that are mathematically designed to mislead AI systems by mimicking the features they associate with entirely different objects. Through the use of a printed design, an AI-supported sensor might see a camouflage covered tank as a tree, despite the object looking nothing like a tree to the human eye. In effect, the camouflage does not just hide an asset, but actively feeds false information to machine vision, causing it to misclassify what it is seeing. Most work on adversarial patterns has so far focused on the visual domain, but there is also potential to develop systems that AI-supported sensors working in other spectrums. 

“I expect adversarial camouflage to become more important… but not replace classical camouflage, rather an addition to it,” says Dr Schwegmann. “At present, adversarial effects are sometimes quite fragile in the real world… as they depend on geometry, sensor type, distance and other factors.”

Advances in AI-supported target detection also have implications for decoys. Poorly designed decoys are likely to become less effective, as AI systems quickly learn to identify the inconsistencies that distinguish them from real assets. However, more sophisticated, data-driven decoys could become even more  valuable, particularly if they are designed to replicate the specific features AI systems are trained to detect.  

Dr Schwegmann says it is now critical for camouflage manufacturers to incorporate AI into both design and testing processes. Using AI allows developers to evaluate how patterns and materials perform against real sensor pipelines and detection algorithms, exposing weaknesses that may not be visible to the human eye. It also enables faster, data-driven optimisation across multiple spectral bands and conditions, ensuring solutions are effective against both human observers and machine vision systems.