On 01/08/2026 01.13, Maria Sophia wrote:
[...]

I'm under the impression there is some
confusion here.

De-noising algorithms, like BM3D or any
de-noising auto-encoder, de-noise just
one image.
There is no temporal noise involved, it
is only spatial, since the algorithms
see only one, single, image.

There are temporal de-noising algorithm
which could be applied to video sequences,
but this does not seem to be the case here.

On the other hand, PRNU will have some
spatial statistical properties, even if
it is fixed for a given sensor.

Now, assuming we have some PRNU from some
unrelated sensor (meaning we can generate
a spatial noise with same statistical
properties), it would be possible to
consider to apply this noise ("apply", not
"add") to a given image.

This means the effective PRNU will be the
combination ("combination", not "sum") of
two PRNUs, resulting in a new fingerprint,
different from the original one.

So, de-noising is not really needed, but it
might be helpful to reduce / remove / modify
the original PRNU, so that the applied one
will be more evident.

The point here is that the image content
will not be altered too much, so quality
will be somehow preserved.
Different than blurring or resizing.

If you do not like BM3D, you can look, as
mentioned above, into de-noising auto-encoders.
These will be even more aggressive in removing
the PRNU content (maybe with more damage to
the overall image).

Finally, consider the "simple" case of generative
algorithm (AI stuff, so to speak).
These could (YMMV) generate image *without* any
PRNU, similar or identical to the one taken
with the camera.
So, the fingerprint will be gone (maybe there will
be another one from the AI).

It seems to me there is a lot to explore way
beyond the simple image modification algorithms.

bye,

--

piergiorgio

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