  Deep neural networks that achieve remarkable performance in image
classification have previously been shown to be easily fooled by tiny
transformations such as a one pixel translation of the input image. In order to
address this problem, two approaches have been proposed in recent years. The
first approach suggests using huge datasets together with data augmentation in
the hope that a highly varied training set will teach the network to learn to
be invariant. The second approach suggests using architectural modifications
based on sampling theory to deal explicitly with image translations. In this
paper, we show that these approaches still fall short in robustly handling
'natural' image translations that simulate a subtle change in camera
orientation. Our findings reveal that a mere one-pixel translation can result
in a significant change in the predicted image representation for approximately
40% of the test images in state-of-the-art models (e.g. open-CLIP trained on
LAION-2B or DINO-v2) , while models that are explicitly constructed to be
robust to cyclic translations can still be fooled with 1 pixel realistic
(non-cyclic) translations 11% of the time. We present Robust Inference by Crop
Selection: a simple method that can be proven to achieve any desired level of
consistency, although with a modest tradeoff with the model's accuracy.
Importantly, we demonstrate how employing this method reduces the ability to
fool state-of-the-art models with a 1 pixel translation to less than 5% while
suffering from only a 1% drop in classification accuracy. Additionally, we show
that our method can be easy adjusted to deal with circular shifts as well. In
such case we achieve 100% robustness to integer shifts with state-of-the-art
accuracy, and with no need for any further training.
