1Columbia University, 2Rutgers University
We introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference. Among different constraints, we find that equivariance-based constraints are most effective. For natural images, equivariance is shared across the input images (left) and the output labels (right), providing a dense constraint. The predictions from a model F(x) should be identical to performing a spatial transformation on x, a forward pass of F, and undoing that spatial transformation on the output space (black).
Representations are equivariant when the predicted images (2nd column) and the reversed prediction of transformed images (3rd, 4th column) are the same. Clean images satisify this naturally while attacked images don't. By optimizing a vector on the attacked images to restore equivariance, our method corrects the prediction.
@article{mao2022robust,
title={Robust Perception through Equivariance},
author={Mao, Chengzhi and Zhang, Lingyu and Joshi, Abhishek
and Yang, Junfeng and Wang, Hao and Vondrick, Carl},
journal={arXiv preprint arXiv:2212.06079},
year={2022}
}
This research is based on work partially supported by the DARPA SAIL-ON program, the NSF NRI award \#1925157, a GE/DARPA grant, a CAIT grant, and gifts from JP Morgan, DiDi, and Accenture. The webpage template was inspired by this project page.