在持续的乌克兰冲突中，俄罗斯军用卡车和飞机采用了引人瞩目的新视觉图案，例如鲜艳的黑白斑马纹以及在机翼上整齐排列的旧轮胎。尽管这种伪装对人类观察者毫无效果，但其目的是为了欺骗乌克兰无人机上的计算机视觉系统。这种现代改装让人联想到皇家海军在第一次世界大战中使用的“眩晕伪装”，当时是为了扰乱船只的轮廓。然而，今天的版本利用了机器视觉的核心机制，该机制依赖于从训练数据中学习到的模式匹配规则。由于斑马纹卡车和堆放轮胎的机翼并不存在于这些数据集中，人工智能很难识别出它们。

这种漏洞被称为系统的“脆弱性”，即对预期特征的微小改动会导致人工智能系统做出极为荒谬的错误分类。旨在利用这些系统缺陷的“对抗性样本”概念已被记录多年；例如，在2017年，麻省理工学院的研究人员通过外壳图案诱导计算机将一只塑料乌龟分类为步枪，而另一项研究则使用道路贴纸欺骗特斯拉的自动驾驶软件驶入逆行车道。在乌克兰，这种掩盖预期特征的战术正在蔓延，俄罗斯无人机也开始涂上眩晕图案，以避免被乌克兰的拦截无人机发现。

随着战场技术的发展和人类操作员被自动化系统取代——仅今年乌克兰就计划生产1000万架无人机——这些欺骗手段势必会更加普遍。这一转变将引发先进的机器视觉系统与日益聪明的伪装手段之间的技术军备竞赛。任何特定图案（如斑马纹）所带来的优势都是暂时的，因为一旦这些变体变得足够普遍，它们就会被纳入新的训练数据集中。最终，尽管双方都在进行调整，开发人员仍在竞相以快于对手修改视觉特征的速度来更新他们的模型。

In the ongoing Ukraine conflict, Russian military vehicles and aircraft have adopted striking new visual patterns, such as vivid black-and-white zebra stripes and rows of old tires on wings. While ineffective against human observers, this camouflage aims to deceive the computer-vision systems on Ukrainian drones. This modern adaptation is reminiscent of the "dazzle camouflage" used by the Royal Navy in World War I, which disrupted ship silhouettes. However, today's version exploits the core mechanism of machine vision, which relies on pattern-matching rules learned from training data. Because zebra-striped trucks and tire-laden wings are absent from these datasets, the artificial intelligence struggles to recognize them.

This vulnerability is known as "brittleness," where minor alterations to expected features cause AI systems to make bizarre misclassifications. The concept of "adversarial examples" designed to exploit these system quirks has been documented for years; for example, in 2017, MIT researchers misled a computer into classifying a plastic turtle as a rifle using shell patterns, while another study used road stickers to trick Tesla's autopilot into oncoming traffic. In Ukraine, the tactic of obscuring expected features is spreading as Russian drones also begin sporting dazzle patterns to avoid detection by Ukrainian interceptor drones.

As battlefield technology evolves and human operators are replaced by automated systems—with Ukraine aiming to produce 10 million drones this year alone—these deception methods are set to proliferate. This shift will trigger a technological arms race between advanced machine vision systems and increasingly clever camouflage. Any advantage gained from specific patterns like zebra stripes is temporary because once these variants become common, they will be incorporated into new training datasets. Ultimately, while both sides adapt, developers are racing to update their models faster than the adversary can modify their visual signatures.

Source: How to hide from killer drones

Subtitle: In the Ukraine war, anti-AI tactics are producing bizarre forms of camouflage

Dateline: Jul 09, 2026 06:37 AM