Russian Forces Use WWI-Era Dazzle Camouflage to Counter AI Drones

The Russian forces are experimenting with Dazzle camouflage, which was used on ships during World Wars I and II, adapting it for military trucks to protect against drones.

The War Zone reported on this.

In recent days, several photos of Russian military trucks with unusual paint schemes have appeared on social media. The spotted examples include Ural and KAMAZ heavy trucks.

At least two versions of this camouflage have been recorded so far: wide contrasting stripes in the style of a zebra and a more complex organic pattern with swirling elements resembling leaves.

In both cases, the paint covers almost the entire external surface of the vehicle, including the wheels and tires.

It is not known whether the white paint is applied over a black base or directly on the standard factory paint.

At first glance, the new camouflage schemes resemble the famous Dazzle camouflage, which the Royal Navy of Great Britain began using on its ships during World War I.

This type of camouflage was developed in 1917 by the official naval artist Norman Wilkinson to reduce the effectiveness of attacks by German submarines.

The basis of Dazzle was contrasting geometric patterns of large blocks of color, most often black and white.

The main purpose of such camouflage was not to hide the ship, but to make it difficult for an observer using a periscope to determine its course, speed, and type.

Even after more advanced rangefinders and radars were introduced, this approach remained somewhat effective.

A similar principle likely underlies the new camouflage on Russian trucks. However, its purpose now is not to mislead a human observer, but to complicate the work of drone cameras and algorithms.

Given that Ukrainian drones increasingly use elements of artificial intelligence to automatically recognize and track targets, Russian forces may be using such camouflage to temporarily reduce the effectiveness of these systems.

However, there is a downside — automatic targeting systems can be trained to recognize such patterns.