Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms

By Qixue Xiao, Yufei Chen, Chao Shen, Yu Chen, Kang Li. 2019

106 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. Luc Van Gool
3. Jun-Yan Zhu
4. Baoyuan Wu
5. Dacheng Tao
6. Ming-Hsuan Yang
7. Yu Qiao
8. Xiaogang Wang

In Safety, Robustness & Interpretability

1. Yong Jiang
2. Baoyuan Wu
3. Konrad Rieck
4. David Klein
5. Zhifeng Li
6. Martin Johns
7. Jun-Yan Zhu
8. 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].”
• Intriguing Properties of Adversarial ML Attacks in the Problem Space (2019, unknown): “, in scaling attacks [73]).”
• 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].”
• Backdoor Learning: A Survey (2020, background): “[21] proposed to conceal the trigger as well as hide the overlays of clean-label poisoning through image-scaling attacks [22].”
• Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning (2020, background): “[35] have shown that scaling algorithms are vulnerable to attacks and can be misused to fool machine learning systems.”