Security of modern Deep Neural Networks (DNNs) is under severe scrutiny as
the deployment of these models become widespread in many intelligence-based
applications. Most recently, DNNs are attacked through Trojan which can
effectively infect the model during the training phase and get activated only
through specific input patterns (i.e, trigger) during inference. In this work,
for the first time, we propose a novel Targeted Bit Trojan(TBT) method, which
can insert a targeted neural Trojan into a DNN through the bit-flip attack. Our
algorithm efficiently generates a trigger specifically designed to locate
certain vulnerable bits of DNN weights stored in main memory (i.e., DRAM). The
objective is that once the attacker flips these vulnerable bits, the network
still operates with normal inference accuracy with benign input. However, when
the attacker activates the trigger by embedding it with any input, the network
is forced to classify all inputs to a certain target class. We demonstrate that
flipping only several vulnerable bits identified by our method, using available
bit-flip techniques (i.e, row-hammer), can transform a fully functional DNN
model into a Trojan-infected model. We perform extensive experiments of
CIFAR-10, SVHN and ImageNet datasets on both VGG-16 and Resnet-18
architectures. Our proposed TBT could classify 92 % of test images to a target
class with as little as 84 bit-flips out of 88 million weight bits on Resnet-18
for CIFAR10 dataset.

本研究提出了一种新的目标二进制特洛伊（TBT）方法，为实现模型的恶意攻击，通过比特翻转攻击将特定的神经特洛伊插入到深度神经网络中。研究表明，只需利用可用的比特翻转技术（即行锤），翻转几个易受攻击的比特就可以将功能完好的 DNN 模型转化为特洛伊感染的模型，并实现将测试图像的 92％分类到目标类的目的。

TBT: 基于位木马的有针对性神经网络攻击

TBT: Targeted Neural Network Attack with Bit Trojan

In stance classification, the target on which the stance is made defines the
boundary of the task, and a classifier is usually trained for prediction on the
same target. In this work, we explore the potential for generalizing
classifiers between different targets, and propose a neural model that can
apply what has been learned from a source target to a destination target. We
show that our model can find useful information shared between relevant targets
which improves generalization in certain scenarios.

本文提出了一种基于神经网络的模型，用于将源目标的学习信息应用于目标分类的问题，表现出了在某些场景下，可为不同目标之间的泛化提供改进的有用信息。