Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow

26 October 2023

Papers citing "Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow"

8 / 8 papers shown

Title
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo Jenny Schmalfuss Victor Oei Lukas Mehl Madlen Bartsch Shashank Agnihotri M. Keuper Andrés Bruhn 31 0 0 14 May 2025
DispBench: Benchmarking Disparity Estimation to Synthetic Corruptions Shashank Agnihotri Amaan Ansari Annika Dackermann Fabian Rösch M. Keuper 53 0 0 08 May 2025
Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions? Shashank Agnihotri David Schader Nico Sharei Mehmet Ege Kaçar M. Keuper 41 2 0 07 May 2025
Attack Anything: Blind DNNs via Universal Background Adversarial Attack Jiawei Lian Shaohui Mei Xiaofei Wang Yi Wang Lefan Wang Yingjie Lu Mingyang Ma Lap-Pui Chau AAML 37 1 0 17 Aug 2024
Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context Shashank Agnihotri Julia Grabinski M. Keuper 30 6 0 29 Nov 2023
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo Lukas Mehl Jenny Schmalfuss Azin Jahedi Yaroslava Nalivayko Andrés Bruhn VGen 36 57 0 03 Mar 2023
Disentangling Architecture and Training for Optical Flow Deqing Sun Charles Herrmann F. Reda Michael Rubinstein David Fleet William T. Freeman 3DPC OOD 66 34 0 21 Mar 2022
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 264 3,110 0 04 Nov 2016