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1610.08401
Cited By
Universal adversarial perturbations
26 October 2016
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
Omar Fawzi
P. Frossard
AAML
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Papers citing
"Universal adversarial perturbations"
50 / 1,266 papers shown
Title
On The Relationship Between Universal Adversarial Attacks And Sparse Representations
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Universal Perturbation-based Secret Key-Controlled Data Hiding
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Wenbiao Yao
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Xiaoqian Chen
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Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
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Ke Sun
Nasimeh Heydaribeni
Seira Hidano
Xinyu Zhang
F. Koushanfar
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01 Nov 2023
LFAA: Crafting Transferable Targeted Adversarial Examples with Low-Frequency Perturbations
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A Survey on Transferability of Adversarial Examples across Deep Neural Networks
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Xiaojun Jia
Pau de Jorge
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Ashkan Khakzar
Zhijiang Li
Xiaochun Cao
Philip Torr
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36
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Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks
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Ananth Balashankar
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Thi Avrahami
Jilin Chen
Alex Beutel
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Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization
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Jingfan Xia
Qi Deng
Luo Luo
31
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0
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Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm
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J. Mondal
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Xi Xiao
Sarfaraz Newaz
AAML
29
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0
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Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks
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Khaled Abud
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E. Shumitskaya
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D. Vatolin
37
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LLMSV
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Bin Xie
Songtao Guo
Yuanyuan Yang
Bin Xiao
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M. Vladimirova
Vincent Fortuin
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Privacy-preserving and Privacy-attacking Approaches for Speech and Audio -- A Survey
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Donald Williamson
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Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
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Yu Ran
Weixuan Tang
Yuan-Gen Wang
34
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AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition
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21
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PRAT: PRofiling Adversarial aTtacks
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Yogesh S Rawat
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35
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The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
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How adversarial attacks can disrupt seemingly stable accurate classifiers
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D. Higham
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37
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Why do universal adversarial attacks work on large language models?: Geometry might be the answer
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MDTD: A Multi Domain Trojan Detector for Deep Neural Networks
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Radha Poovendran
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29
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On the Robustness of Object Detection Models on Aerial Images
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Jian Ding
Gui-Song Xia
Gui-Song Xia
40
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Can We Rely on AI?
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45
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On-Manifold Projected Gradient Descent
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Harbir Antil
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18
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DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization
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Zhe Xin
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AIR: Threats of Adversarial Attacks on Deep Learning-Based Information Recovery
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Keqiang Yue
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23
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Test-Time Poisoning Attacks Against Test-Time Adaptation Models
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Yun Shen
Yang Zhang
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Hai Jin
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Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
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A reading survey on adversarial machine learning: Adversarial attacks and their understanding
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SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation
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Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches
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Transferable Attack for Semantic Segmentation
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Beating Backdoor Attack at Its Own Game
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Universal and Transferable Adversarial Attacks on Aligned Language Models
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When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-
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Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations
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