As deep neural networks and the datasets used to train them get larger, the
default approach to integrating them into research and commercial projects is
to download a pre-trained model and fine tune it. But these models can have
uncertain provenance, opening up the possibility that they embed hidden
malicious behavior such as trojans or backdoors, where small changes to an
input (triggers) can cause the model to produce incorrect outputs (e.g., to
misclassify). This paper introduces a novel approach to backdoor detection that
uses two tensor decomposition methods applied to network activations. This has
a number of advantages relative to existing detection methods, including the
ability to analyze multiple models at the same time, working across a wide
variety of network architectures, making no assumptions about the nature of
triggers used to alter network behavior, and being computationally efficient.
We provide a detailed description of the detection pipeline along with results
on models trained on the MNIST digit dataset, CIFAR-10 dataset, and two
difficult datasets from NIST's TrojAI competition. These results show that our
method detects backdoored networks more accurately and efficiently than current
state-of-the-art methods.

这篇论文介绍了一种新的后门检测方法，它使用了两种张量分解方法应用于网络激活，相对于现有的检测方法具有多种优势，包括能够同时分析多个模型，适用于各种网络架构，对于改变网络行为的触发器不做任何假设，并且计算效率高。在 MNIST 手写数字数据集、CIFAR-10 数据集以及 NIST 的 TrojAI 比赛中的两个难数据集上，我们提供了检测流程的详细描述和结果。这些结果表明，相对于当前最先进的方法，我们的方法更准确、更高效地检测到了植入后门的网络。

TEN-GUARD: 张量分解用于深度神经网络中的后门攻击检测

TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep  Neural Networks

The widespread use of multisensor technology and the emergence of big
datasets have created the need to develop tools to reduce, approximate, and
classify large and multimodal data such as higher-order tensors. While early
approaches focused on matrix and vector based methods to represent these
higher-order data, more recently it has been shown that tensor decomposition
methods are better equipped to capture couplings across their different modes.
For these reasons, tensor decomposition methods have found applications in many
different signal processing problems including dimensionality reduction, signal
separation, linear regression, feature extraction, and classification. However,
most of the existing tensor decomposition methods are based on the principle of
finding a low-rank approximation in a linear subspace structure, where the
definition of the rank may change depending on the particular decomposition.
Since many datasets are not necessarily low-rank in a linear subspace, this
often results in high approximation errors or low compression rates. In this
paper, we introduce a new adaptive, multi-scale tensor decomposition method for
higher order data inspired by hybrid linear modeling and subspace clustering
techniques. In particular, we develop a multi-scale higher-order singular value
decomposition (MS-HoSVD) approach where a given tensor is first permuted and
then partitioned into several sub-tensors each of which can be represented as a
low-rank tensor with increased representational efficiency. The proposed
approach is evaluated for dimensionality reduction and classification for
several different real-life tensor signals with promising results.

介绍了一种基于混合线性建模和子空间聚类技术的自适应、多尺度张量分解方法，旨在降低大型和多模态数据的维度和表示复杂度。该方法在多个真实张量信号的维数约简和分类问题中表现良好。