CCTV system scanning cars

The Challenge

Many organisations are reaping the rewards of AI/machine learning systems but are increasingly concerned about potential cyber threats directed at AI components. We were tasked to assess the ease with which adversarial techniques could be used to ‘fool’ AI systems used in applications such as video surveillance.

Two adversarial patches one of a dog and one of an ambulance

The Approach

We developed adversarial patches – 2D images to be attached to the flat surface of a 3D object.  These patches were able to fool AI image classifiers, creating false-positives by placement on roads, attachment to walls, display on clothing or on accessories such as bags/hats.

A peacock adversarial patch on the side of a blue car


We provided demonstrations that illustrate adversarial patch attacks in the customer’s operational environment. We’ve since further developed concepts to build other adversarial attacks and novel defence measures, all operating within real-world environments and the associated cyber threats.

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