@inproceedings{
langosco2024detecting,
title={Detecting Backdoors with Meta-Models},
author={Lauro Langosco and Neel Alex and William Baker and David Quarel and Herbie Bradley and David Krueger},
booktitle={NeurIPS 2023 Workshop on Backdoors in Deep Learning - The Good, the Bad, and the Ugly},
year={2024},
url={https://openreview.net/forum?id=cmJiEqniEc}
}
It is widely known that it is possible to implant backdoors into neural networks,
by which an attacker can choose an input to produce a particular undesirable output 
(e.g.\ misclassify an image).
We propose to use \emph{meta-models}, neural networks that take another network's parameters 
as input, to detect backdoors directly from model weights.
To this end we present a meta-model architecture and train it on a dataset of approx.\ 4000 clean and backdoored CNNs trained on CIFAR-10.
Our approach is simple and scalable, and is able to detect the presence of a backdoor with $>99\%$ accuracy when the test trigger pattern is i.i.d., with some success even on out-of-distribution backdoors.
Detecting Backdoors with Meta-Models
Lauro Langosco Neel Alex William Baker David Quarel Herbie Bradley David Krueger