Deep Image Destruction: Vulnerability of Deep Image-to-Image Models
against Adversarial Attacks

Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks

30 April 2021

Jun-Ho Choi

Jun-Hyuk Kim

Papers citing "Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks"

8 / 8 papers shown

Title
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix Shashank Agnihotri Kanchana Vaishnavi Gandikota Julia Grabinski Paramanand Chandramouli M. Keuper 30 9 0 25 Jul 2023
Learning Provably Robust Estimators for Inverse Problems via Jittering Anselm Krainovic Mahdi Soltanolkotabi Reinhard Heckel OOD 6 6 0 24 Jul 2023
Physics-Driven Turbulence Image Restoration with Stochastic Refinement Ajay Jaiswal Xingguang Zhang Stanley H. Chan Zhangyang Wang 13 20 0 20 Jul 2023
Fooling the Image Dehazing Models by First Order Gradient Jie Gui Xiaofeng Cong Chengwei Peng Yuan Yan Tang James T. Kwok AAML 11 8 0 30 Mar 2023
Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality Guy Ohayon Theo Adrai Michael Elad T. Michaeli AAML 29 13 0 16 Nov 2022
Imaging through the Atmosphere using Turbulence Mitigation Transformer Xingguang Zhang Zhiyuan Mao Nicholas Chimitt Stanley H. Chan ViT 13 30 0 13 Jul 2022
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning Tong Chen Zhan Ma AAML 14 28 0 16 Dec 2021
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 256 3,108 0 04 Nov 2016