Processing and acquisition traces in visual encoders: What does CLIP know about your camera?
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914436392943616 |
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| author | Ramos, Ryan Stojnić, Vladan Kordopatis-Zilos, Giorgos Nakashima, Yuta Tolias, Giorgos Garcia, Noa |
| author_facet | Ramos, Ryan Stojnić, Vladan Kordopatis-Zilos, Giorgos Nakashima, Yuta Tolias, Giorgos Garcia, Noa |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Processing and acquisition traces in visual encoders: What does CLIP know about your camera? Ramos, Ryan Stojnić, Vladan Kordopatis-Zilos, Giorgos Nakashima, Yuta Tolias, Giorgos Garcia, Noa Computer Vision and Pattern Recognition 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 |
| title | Processing and acquisition traces in visual encoders: What does CLIP know about your camera? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.10637 |