First of all, there is no such thing as a non-strided convolution. Using the low-level API, the following statement will give an error because every element in strides argument must be greater than zero!
y = tf.nn.conv2d(x, strides=[1, 0, 0, 1]) # error !
y = tf.nn.conv2d(x, strides=[1, 1, 1, 1]) # OK
Note that the first (=sample index) and the last (=channel) dimension must equal to one.
Secondly, when using the TF Layers API, the strides argument has a default value:
Note that there are only two entries in the strides tuple which correspond to the second and the third entry in the Layers API. The first and the last dimension are dropped.
So if you haven't set the strides argument, the convolution filters will move with of one pixel by default and I suppose this is what they mean with non-strided convolutions in the paper.