  CLIP is a widely used foundational vision-language model that is used for
zero-shot image recognition and other image-text alignment tasks. We
demonstrate that CLIP is vulnerable to change in image quality under
compression. This surprising result is further analysed using an attribution
method-Integrated Gradients. Using this attribution method, we are able to
better understand both quantitatively and qualitatively exactly the nature in
which the compression affects the zero-shot recognition accuracy of this model.
We evaluate this extensively on CIFAR-10 and STL-10. Our work provides the
basis to understand this vulnerability of CLIP and can help us develop more
effective methods to improve the robustness of CLIP and other vision-language
models.
