1.7k citations · 2.2k across the 3 of their papers we have counts for
4 papers · 1 filter
Large-Scale Public Data Improves Differentially Private Image Generation Quality
Ruihan Wu, Chuan Guo, Kamalika Chaudhuri
Public data has been frequently used to improve the privacy-accuracy trade-off of differentially private machine learning, but prior work largely assumes that this data come from t…
ViP: A Differentially Private Foundation Model for Computer Vision
Yaodong Yu, Maziar Sanjabi, Yi Ma +2
Artificial intelligence (AI) has seen a tremendous surge in capabilities thanks to the use of foundation models trained on internet-scale data. On the flip side, the uncurated natu…
Low Frequency Adversarial Perturbation
Chuan Guo, Jared S. Frank, Kilian Q. Weinberger
Adversarial images aim to change a target model's decision by minimally perturbing a target image. In the black-box setting, the absence of gradient information often renders this…
Countering Adversarial Images using Input Transformations
Chuan Guo, Mayank Rana, Moustapha Cisse +1
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Spe…