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Generating Semantic Adversarial Examples via Feature Manipulation
v1v2 (latest)

Generating Semantic Adversarial Examples via Feature Manipulation

6 January 2020
Shuo Wang
Surya Nepal
Carsten Rudolph
M. Grobler
Shangyu Chen
Tianle Chen
    AAML
ArXiv (abs)PDFHTML

Papers citing "Generating Semantic Adversarial Examples via Feature Manipulation"

6 / 6 papers shown
Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks
Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks
Stephen Casper
K. Hariharan
Dylan Hadfield-Menell
AAML
461
11
0
18 Nov 2022
Towards Compositional Adversarial Robustness: Generalizing Adversarial
  Training to Composite Semantic Perturbations
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic PerturbationsComputer Vision and Pattern Recognition (CVPR), 2022
Lei Hsiung
Yun-Yun Tsai
Pin-Yu Chen
Tsung-Yi Ho
AAML
312
45
0
09 Feb 2022
Robust Feature-Level Adversaries are Interpretability Tools
Robust Feature-Level Adversaries are Interpretability Tools
Stephen Casper
Max Nadeau
Dylan Hadfield-Menell
Gabriel Kreiman
AAML
794
34
0
07 Oct 2021
Exposing Previously Undetectable Faults in Deep Neural Networks
Exposing Previously Undetectable Faults in Deep Neural NetworksInternational Symposium on Software Testing and Analysis (ISSTA), 2021
Isaac Dunn
Hadrien Pouget
Daniel Kroening
T. Melham
AAML
324
34
0
01 Jun 2021
Synthetic Observational Health Data with GANs: from slow adoption to a
  boom in medical research and ultimately digital twins?
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
Jeremy Georges-Filteau
Elisa Cirillo
SyDaAI4CE
496
18
0
27 May 2020
Generating Natural Adversarial Examples
Generating Natural Adversarial Examples
Zhengli Zhao
Dheeru Dua
Sameer Singh
GANAAML
834
653
0
31 Oct 2017
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