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Most artificially intelligent systems are based on neural networks, algorithms inspired by biological neurons found in the brain. These networks can consist of multiple layers, with inputs coming in one side and outputs going out of the other. The outputs can be used to make automatic decisions, for example, in driverless cars. Attacks to mislead a neural network can involve exploiting vulnerabilities in the input layers, but typically only the initial input layer is considered when engineering a defense. For the first time, researchers augmented a neural network’s inner layers with a process involving random noise to improve its resilience.

Bunkyo kuJumpei ukitaKenichi ohkiRohan mehraOhki labDepartment of physiologyPublic relations groupTakeda science foundationSchool of medicineJapan agency for medical researchUniversity of tokyo is japanMethod of researchUniversity of tokyoNeural networksUniversity of tokyo graduate school medicineInternational research center