面向人工智能模型的安全攻击和防御策略综述.pdf
计算机研究与发展DOI：10.7544/issn1000-1239.202440449
Journal
of
Computer
Research
and
Development61（10）：2627−2648，2024
面向人工智能模型的安全攻击和防御策略综述
秦臻庄添铭朱国淞周尔强丁熠耿技
（网络与数据安全四川省重点实验室（电子科技大学）成都610054）
（zhoueq@）
SurveyofSecurityAttackandDefenseStrategiesforArtificialIntelligenceModel
Qin
Zhen,
Zhuang
Tianming,
Zhu
Guosong,
Zhou
Erqiang,
Ding
Yi,
and
Geng
Ji
（NetworkandDataSecurityKeyLaboratoryofSichuanProvince(UniversityofElectronicScienceandTechnologyofChina),Chengdu
610054）
AbstractIn
recent
years,
the
rapid
development
of
artificial
intelligence
technology,
particularly
deep
learning,
has
led
to
its
widespread
application
in
various
fields
such
as
computer
vision
and
natural
language
processing.
However,
recent
research
indicates
potential
security
risks
associated
with
these
advanced
AI
models
could
compromise
their
reliability.
In
light
of
this
concern,
this
survey
delves
into
cutting-edge
research
findings
pertaining
to
security
attacks,
attack
detection,
and
defense
strategies
for
artificial
intelligence
models.
Specifically
regarding
model
security
attacks,
our
work
focuses
on
elucidating
the
principles
and
technical
status
of
adversarial
attacks,
model
inversion
attacks,
and
model
theft
attacks.
With
regards
to
model
attack
detection
methods
explored
in
this
paper,
they
include
defensive
distillation
techniques,
regularization
approaches,
outlier
detection,
robust
statistics.
As
for
model
defense
strategies
examined
in
this
study,
they
encompass
adversarial
training
measures,
model
structure
defense
mechanisms,
query
control
defenses
along
with
other
technical
means.
This
comprehensive
survey
not
only
summarizes
but
also
expands
upon
techniques
and
methodologies
rel