Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor
attacks, where attackers embed hidden backdoors in the DNN model by injecting a
few poisoned examples into the training dataset. While extensive efforts have
been made to detect and remove backdoors from backdoored DNNs, it is still not
clear whether a backdoor-free clean model can be directly obtained from
poisoned datasets. In this paper, we first construct a causal graph to model
the generation process of poisoned data and find that the backdoor attack acts
as the confounder, which brings spurious associations between the input images
and target labels, making the model predictions less reliable. Inspired by the
causal understanding, we propose the Causality-inspired Backdoor Defense (CBD),
to learn deconfounded representations for reliable classification.
Specifically, a backdoored model is intentionally trained to capture the
confounding effects. The other clean model dedicates to capturing the desired
causal effects by minimizing the mutual information with the confounding
representations from the backdoored model and employing a sample-wise
re-weighting scheme. Extensive experiments on multiple benchmark datasets
against 6 state-of-the-art attacks verify that our proposed defense method is
effective in reducing backdoor threats while maintaining high accuracy in
predicting benign samples. Further analysis shows that CBD can also resist
potential adaptive attacks. The code is available at
https://github.com/zaixizhang/CBD.

本文提出了一种因果性启发的后门防御方法（CBD），通过构建因果图模型污染数据的生成过程，并将后门攻击作为混淆因素，利用混淆模型学习去混淆的特征表示，最终实现可靠的分类预测。经过多个基准数据集的实验验证，CBD 方法有效地减少了后门威胁，同时在预测良性样本的准确性方面表现出高水平。

通过去混淆表示学习进行后门防御

Backdoor Defense via Deconfounded Representation Learning

Real-world imagery is often characterized by a significant imbalance of the
number of images per class, leading to long-tailed distributions. An effective
and simple approach to long-tailed visual recognition is to learn feature
representations and a classifier separately, with instance and class-balanced
sampling, respectively. In this work, we introduce a new framework, by making
the key observation that a feature representation learned with instance
sampling is far from optimal in a long-tailed setting. Our main contribution is
a new training method, referred to as Class-Balanced Distillation (CBD), that
leverages knowledge distillation to enhance feature representations. CBD allows
the feature representation to evolve in the second training stage, guided by
the teacher learned in the first stage. The second stage uses class-balanced
sampling, in order to focus on under-represented classes. This framework can
naturally accommodate the usage of multiple teachers, unlocking the information
from an ensemble of models to enhance recognition capabilities. Our experiments
show that the proposed technique consistently outperforms the state of the art
on long-tailed recognition benchmarks such as ImageNet-LT, iNaturalist17 and
iNaturalist18.

提出了一种新的框架 CBD，利用知识蒸馏增强特征表示，通过使用类平衡采样，第二阶段的训练使得特征表示能够在少数类上进化，可以自然的适用多个 Teachers，并在 ImageNet-LT，iNaturalist17 和 iNaturalist18 数据集上取得比现有技术更好的性能。