Regula Sub rosa Latent Backdoor Attacks on Deep Neural Networks

Recent work has proposed the concept of backdoor attacks on deep neural networks, where misbehaviors are hidden inside “normal” models, only to be triggered by very specific inputs . In practice, these attacks are difficult to perform and highly constrained by sharing of models through transfer learning . In this paper, we describe a significantly more powerful variant of the backdoor attack, latent backdoors, where hidden rules can be embedded in a single “Teacher” model, and automatically inherited by all “Student” models through the transfer learning process . We show that latent backdoor can be quite effective in a variety of application contexts, and validate its practicality through real-world attacks against traffic sign recognition, iris identification of lab volunteers, and facial recognition of public figures . Finally, we evaluate 4 potential defenses, and find that only one is effective in

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Keywords : backdoor - attacks - models - latent - hidden -

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