Processing and acquisition traces in visual encoders: What does CLIP
  know about your camera?
Authors: name: Ryan Ramos, name: Vladan Stojnić, name: Giorgos Kordopatis-Zilos, name: Yuta Nakashima, name: Giorgos Tolias, name: Noa Garcia

Prior work has analyzed the robustness of visual encoders to image
transformations and corruptions, particularly in cases where such alterations
are not seen during training. When this occurs, they introduce a form of
distribution shift at test time, often leading to performance degradation. The
primary focus has been on severe corruptions that, when applied aggressively,
distort useful signals necessary for accurate semantic predictions.
  We take a different perspective by analyzing parameters of the image
acquisition process and transformations that may be subtle or even
imperceptible to the human eye. We find that such parameters are systematically
encoded in the learned visual representations and can be easily recovered. More
strikingly, their presence can have a profound impact, either positively or
negatively, on semantic predictions. This effect depends on whether there is a
strong correlation or anti-correlation between semantic labels and these
acquisition-based or processing-based labels. Our code and data are available
at: https://github.com/ryan-caesar-ramos/visual-encoder-traces