Architectural Neural Backdoors from First Principles

While previous research backdoored neural networks by changing their parameters, recent work uncovered a more insidious threat: backdoors embedded within the definition of the network's architecture. This involves injecting common architectural components, such as activation functions and pooling layers, to subtly introduce a backdoor behavior that persists even after (full re-)training. However, the full scope and implications of architectural backdoors have remained largely unexplored. Bober-Irizar et al. [2023] introduced the first architectural backdoor; they showed how to create a backdoor …

architecture backdoor behavior components cs.ai cs.cr cs.cv cs.lg definition embedded functions insidious network networks neural networks parameters pooling research threat training work