Deep learning models, increasingly integral to safety-critical systems like self-driving cars and medical devices, are vulnerable to stealthy backdoor attacks. These attacks involve injecting hidden triggers into models, causing them to misbehave when triggered. Researchers from the Qatar Computing Research Institute and the Mohamed bin Zayed University of Artificial Intelligence have developed DeBackdoor, a novel […] The post DeBackdoor: A Framework for Detecting Backdoor Attacks in Deep Learning Models appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.
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