Deceiving AI

 Made me think, usually models we create are to determine some state, current or future, to be more accurate.   But now we can make models that have more precisely deceptive results.     Yes, can see why DARPA is interested.  Includes  a visual overview.

Deceiving AI   By Don Monroe

Communications of the ACM, June 2021, Vol. 64 No. 6, Pages 15-16 10.1145/3460218

Over the last decade, deep learning systems have shown an astonishing ability to classify images, translate languages, and perform other tasks that once seemed uniquely human. However, these systems work opaquely and sometimes make elementary mistakes, and this fragility could be intentionally exploited to threaten security or safety.

In 2018, for example, a group of undergraduates at the Massachusetts Institute of Technology (MIT) three-dimensionally (3D) printed a toy turtle that Google's Cloud Vision system consistently classified as a rifle, even when viewed from various directions. Other researchers have tweaked an ordinary-sounding speech segment to direct a smart speaker to a malicious website. These misclassifications sound amusing, but they could also represent a serious vulnerability as machine learning is widely deployed in medical, legal, and financial systems.

The potential vulnerabilities extend to military systems, said Hava Siegelman of the University of Massachusetts, Amherst. Siegelman initiated a program called Guaranteed AI Robustness against Deception (GARD) while she was on assignment to the U.S. Defense Advanced Research Projects Agency (DARPA). To illustrate the issue to colleagues there, she said, "I showed them an example that I did, and they all started screaming that the room was not secure enough." The examples she shares publicly are worrisome enough, though, such as a tank adorned with tiny pictures of cows that cause an artificial intelligence (AI)-based vision system to perceive it to be as a herd of cows because, she said, AI "works on the surfaces."

The current program manager for GARD at DARPA, Bruce Draper of Colorado State University, is more sanguine. "We have not yet gotten to that point where there's something out there that has happened that has given me nightmares," he said, adding, "We're trying to head that off."

Researchers, some with funding from DARPA, are actively exploring ways to make machine learning more robust against adversarial attacks, and to understand the principles and limitations of these approaches. In the real world, these techniques are likely to be one piece of an ongoing, multilayered security strategy that will slow attackers but not stop them entirely. "It's an AI problem, but it's also a security problem," Draper said. ... '