All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines
Yuxuan Zhang, Bo Dong, Felix Heide

TL;DR
This paper introduces a RAW-based preprocessing defense for neural networks that leverages the natural image acquisition process to effectively mitigate adversarial attacks across multiple vision tasks without retraining.
Contribution
It proposes a model-agnostic RAW-to-RGB pipeline that enhances adversarial robustness by exploiting the natural image capture process, without requiring adversarial training.
Findings
Outperforms existing defenses on ImageNet and COCO datasets
Effective across classification, segmentation, and detection tasks
Does not require retraining for new tasks
Abstract
Existing neural networks for computer vision tasks are vulnerable to adversarial attacks: adding imperceptible perturbations to the input images can fool these methods to make a false prediction on an image that was correctly predicted without the perturbation. Various defense methods have proposed image-to-image mapping methods, either including these perturbations in the training process or removing them in a preprocessing denoising step. In doing so, existing methods often ignore that the natural RGB images in today's datasets are not captured but, in fact, recovered from RAW color filter array captures that are subject to various degradations in the capture. In this work, we exploit this RAW data distribution as an empirical prior for adversarial defense. Specifically, we proposed a model-agnostic adversarial defensive method, which maps the input RGB images to Bayer RAW space and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Bacillus and Francisella bacterial research
