Resizing and aliasing in computer science
When you resize to a lower resolution, there are several ways to do
it. If you average values of the pixels that merge into a single one,
you simply have a loss of resolution, which amounts to keeping only
the lower spatial frequencies (counting frequencies relative to image
size, not to pixel size).  However, if you reduce resoution by
sampling, i.e. replacing a bunch of pixels by the value of one of
them, you may overplay high frequencies, not meaningful at the given sampling (spatial) frequency, leading to all kinds of
artefacts such as moiré effects for images.

When you recreate the image at former resolution, you cannot in
principle recreate
lost information. But you try to make educated guess as to the effect
most likely to occur or not to occur in most images. Hence, you apply
some reprocessing to the image so as to eliminate unlikely phenomena,
or play down phenomena that you know could not have survived the initial size
reduction (they may or may not have been there, but you know that
later presence is not due to original existence).

Typically, as you know that the image reduction has necessarily lost
some higher spatial frequencies, or distorted their role, you can
choose to eliminate or play doen those frequencies at the recreated higher
resolution, as they do not originate from the initial image.

For example, if you had a large geometric image, reduction and back
resizing will produce a staircase effect on most straight lines. These
abrupt changes correspond to high spatial bandwith. By removing these high
frequencies, you will recreate shaded areas that smooth down the staircase effect on the
straight lines.

Aliasing has to do with the artefact due to the combined effect of
sampling and reproducing back a sampled observation of a theoretically
continuous phenomenon (or considered as such because its initial resolution is
significantly greater than the sampling rate), may be an image, a
sound, a film,etc. As seen above, the sampling technique does matter.
Antialiasing consist of the techniques used to eliminate
artefacts known as likely to occur.  It cannot recreate lost
information unless you knew separately that the processed information
has to satisfy some constraints, which antialiasing can then use to
improve the image. For example, you may know that geometric images use
straight lines more often than small staircases.

Wikipedia has more technical information.