Making contactless payments using a smartwatch is increasingly popular, but
this payment medium lacks traditional biometric security measures such as
facial or fingerprint recognition. In 2022, Sturgess et al. proposed WatchAuth,
a system for authenticating smartwatch payments using the physical gesture of
reaching towards a payment terminal. While effective, the system requires the
user to undergo a burdensome enrolment period to achieve acceptable error
levels. In this dissertation, we explore whether applications of deep learning
can reduce the number of gestures a user must provide to enrol into an
authentication system for smartwatch payment. We firstly construct a
deep-learned authentication system that outperforms the current
state-of-the-art, including in a scenario where the target user has provided a
limited number of gestures. We then develop a regularised autoencoder model for
generating synthetic user-specific gestures. We show that using these gestures
in training improves classification ability for an authentication system.
Through this technique we can reduce the number of gestures required to enrol a
user into a WatchAuth-like system without negatively impacting its error rates.

探索深度学习技术是否可以降低用户在智能手表支付认证系统中提供手势的次数，通过使用自动生成的仿真手势训练可以减少用户在类似 WatchAuth 系统中的注册手势数量而不影响其错误率。

利用深度学习提高智能手表支付的安全性

Improving the Security of Smartwatch Payment with Deep Learning

Honeywords are fictitious passwords inserted into databases in order to
identify password breaches. The major difficulty is how to produce honeywords
that are difficult to distinguish from real passwords. Although the generation
of honeywords has been widely investigated in the past, the majority of
existing research assumes attackers have no knowledge of the users. These
honeyword generating techniques (HGTs) may utterly fail if attackers exploit
users' personally identifiable information (PII) and the real passwords include
users' PII. In this paper, we propose to build a more secure and trustworthy
authentication system that employs off-the-shelf pre-trained language models
which require no further training on real passwords to produce honeywords while
retaining the PII of the associated real password, therefore significantly
raising the bar for attackers.
We conducted a pilot experiment in which individuals are asked to distinguish
between authentic passwords and honeywords when the username is provided for
GPT-3 and a tweaking technique. Results show that it is extremely difficult to
distinguish the real passwords from the artifical ones for both techniques. We
speculate that a larger sample size could reveal a significant difference
between the two HGT techniques, favouring our proposed approach.

使用预训练语言模型构建的身份验证系统，生成包含个人身份信息的 honeywords，难以被攻击者区分，从而提高密码保护的安全性。

利用语言模型生成针对性蜜语

Targeted Honeyword Generation with Language Models

Deep learning models have achieved high performance on many tasks, and thus
have been applied to many security-critical scenarios. For example, deep
learning-based face recognition systems have been used to authenticate users to
access many security-sensitive applications like payment apps. Such usages of
deep learning systems provide the adversaries with sufficient incentives to
perform attacks against these systems for their adversarial purposes. In this
work, we consider a new type of attacks, called backdoor attacks, where the
attacker's goal is to create a backdoor into a learning-based authentication
system, so that he can easily circumvent the system by leveraging the backdoor.
Specifically, the adversary aims at creating backdoor instances, so that the
victim learning system will be misled to classify the backdoor instances as a
target label specified by the adversary. In particular, we study backdoor
poisoning attacks, which achieve backdoor attacks using poisoning strategies.
Different from all existing work, our studied poisoning strategies can apply
under a very weak threat model: (1) the adversary has no knowledge of the model
and the training set used by the victim system; (2) the attacker is allowed to
inject only a small amount of poisoning samples; (3) the backdoor key is hard
to notice even by human beings to achieve stealthiness. We conduct evaluation
to demonstrate that a backdoor adversary can inject only around 50 poisoning
samples, while achieving an attack success rate of above 90%. We are also the
first work to show that a data poisoning attack can create physically
implementable backdoors without touching the training process. Our work
demonstrates that backdoor poisoning attacks pose real threats to a learning
system, and thus highlights the importance of further investigation and
proposing defense strategies against them.

该论文研究了基于深度学习的认证系统对于后门攻击的脆弱性，特别是通过数据注入的后门攻击，通过注入极少的毒数据，攻击者可以成功实现攻击，进而窃取敏感信息，从而为后续研究防御策略提供了研究价值。