AI-Generated
Steganography
New
research
suggests
that
AIs
can
produce
perfectly
secure
steganographic
images:
Abstract:
Steganography
is
the
practice
of
encoding
secret
information
into
innocuous
content
in
such
a
manner
that
an
adversarial
third
party
would
not
realize
that
there
is
hidden
meaning.
While
this
problem
has
classically
been
studied
in
security
literature,
recent
advances
in
generative
models
have
led
to
a
shared
interest
among
security
and
machine
learning
researchers
in
developing
scalable
steganography
techniques.
In
this
work,
we
show
that
a
steganography
procedure
is
perfectly
secure
under
Cachin
(1998)’s
information
theoretic-model
of
steganography
if
and
only
if
it
is
induced
by
a
coupling.
Furthermore,
we
show
that,
among
perfectly
secure
procedures,
a
procedure
is
maximally
efficient
if
and
only
if
it
is
induced
by
a
minimum
entropy
coupling.
These
insights
yield
what
are,
to
the
best
of
our
knowledge,
the
first
steganography
algorithms
to
achieve
perfect
security
guarantees
with
non-trivial
efficiency;
additionally,
these
algorithms
are
highly
scalable.
To
provide
empirical
validation,
we
compare
a
minimum
entropy
coupling-based
approach
to
three
modern
baselines—arithmetic
coding,
Meteor,
and
adaptive
dynamic
grouping—using
GPT-2,
WaveRNN,
and
Image
Transformer
as
communication
channels.
We
find
that
the
minimum
entropy
coupling-based
approach
achieves
superior
encoding
efficiency,
despite
its
stronger
security
constraints.
In
aggregate,
these
results
suggest
that
it
may
be
natural
to
view
information-theoretic
steganography
through
the
lens
of
minimum
entropy
coupling.
News
article.
Sidebar
photo
of
Bruce
Schneier
by
Joe
MacInnis.
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