Researchers have unveiled ONEFLIP, a novel inference-time backdoor attack that compromises full-precision deep neural networks (DNNs) by flipping just one bit in the model’s weights, marking a significant escalation in the practicality of hardware-based attacks on AI systems. Unlike traditional backdoor methods that require poisoning training data or manipulating the training process, ONEFLIP operates during […]

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Aman Mishra

Source: gbHackers
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