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Jan 16, 2020

Software detects backdoor attacks on facial recognition

Posted by in categories: cybercrime/malcode, engineering, military, robotics/AI

As the U.S. Army increasingly uses facial and object recognition to train artificial intelligent systems to identify threats, the need to protect its systems from cyberattacks becomes essential.

An Army project conducted by researchers at Duke University and led by electrical and computer engineering faculty members Dr. Helen Li and Dr. Yiran Chen, made significant progress toward mitigating these types of attacks. Two members of the Duke team, Yukun Yang and Ximing Qiao, recently took first prize in the Defense category of the CSAW ‘19 HackML competition.

“Object recognition is a key component of future intelligent systems, and the Army must safeguard these systems from cyberattacks,” said MaryAnne Fields, program manager for intelligent systems at the Army Research Office. “This work will lay the foundations for recognizing and mitigating backdoor attacks in which the data used to train the system is subtly altered to give incorrect answers. Safeguarding object recognition systems will ensure that future Soldiers will have confidence in the intelligent systems they use.”

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