Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning

By Erwin Quiring, David Klein, Daniel Arp, Martin Johns, Konrad Rieck. 2020

83 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Richard Zhang
2. Jun-Yan Zhu
3. Luc Van Gool
4. Gaurav Parmar
5. Ming-Hsuan Yang
6. Trevor Darrell
7. Dacheng Tao
8. Alexei A. Efros

In Safety, Robustness & Interpretability

1. Jun-Yan Zhu
2. Richard Zhang

Citations in the corpus

• On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation (2021, background): “Without proper antialiasing, in the worst case, an adversary can embed a completely different image in the original, resulting in a “scaling attack” [58, 75].”
• On Aliased Resizing and Surprising Subtleties in GAN Evaluation (2022, background): “Without proper antialiasing, in the worst case, an adversary can embed a completely different image in the original, resulting in a “scaling attack” [53, 69].”