  Machine learning models have been found to learn shortcuts -- unintended
decision rules that are unable to generalize -- undermining models'
reliability. Previous works address this problem under the tenuous assumption
that only a single shortcut exists in the training data. Real-world images are
rife with multiple visual cues from background to texture. Key to advancing the
reliability of vision systems is understanding whether existing methods can
overcome multiple shortcuts or struggle in a Whac-A-Mole game, i.e., where
mitigating one shortcut amplifies reliance on others. To address this
shortcoming, we propose two benchmarks: 1) UrbanCars, a dataset with precisely
controlled spurious cues, and 2) ImageNet-W, an evaluation set based on
ImageNet for watermark, a shortcut we discovered affects nearly every modern
vision model. Along with texture and background, ImageNet-W allows us to study
multiple shortcuts emerging from training on natural images. We find computer
vision models, including large foundation models -- regardless of training set,
architecture, and supervision -- struggle when multiple shortcuts are present.
Even methods explicitly designed to combat shortcuts struggle in a Whac-A-Mole
dilemma. To tackle this challenge, we propose Last Layer Ensemble, a
simple-yet-effective method to mitigate multiple shortcuts without Whac-A-Mole
behavior. Our results surface multi-shortcut mitigation as an overlooked
challenge critical to advancing the reliability of vision systems. The datasets
and code are released: https://github.com/facebookresearch/Whac-A-Mole.
