  The utilization of pre-trained networks, especially those trained on
ImageNet, has become a common practice in Computer Vision. However, prior
research has indicated that a significant number of images in the ImageNet
dataset contain watermarks, making pre-trained networks susceptible to learning
artifacts such as watermark patterns within their latent spaces. In this paper,
we aim to assess the extent to which popular pre-trained architectures display
such behavior and to determine which classes are most affected. Additionally,
we examine the impact of watermarks on the extracted features. Contrary to the
popular belief that the Chinese logographic watermarks impact the "carton"
class only, our analysis reveals that a variety of ImageNet classes, such as
"monitor", "broom", "apron" and "safe" rely on spurious correlations. Finally,
we propose a simple approach to mitigate this issue in fine-tuned networks by
ignoring the encodings from the feature-extractor layer of ImageNet pre-trained
networks that are most susceptible to watermark imprints.
