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1608.04644
Cited By
Towards Evaluating the Robustness of Neural Networks
16 August 2016
Nicholas Carlini
D. Wagner
OOD
AAML
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Papers citing
"Towards Evaluating the Robustness of Neural Networks"
50 / 1,465 papers shown
Title
Backpropagation Path Search On Adversarial Transferability
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Jianping Zhang
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Changhua Meng
Weiqiang Wang
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5
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Guy Gaziv
Michael J. Lee
J. DiCarlo
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24
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0
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Face Encryption via Frequency-Restricted Identity-Agnostic Attacks
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Rui Wang
Siyuan Liang
Aishan Liu
Lihua Jing
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PICV
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8
0
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CGBA: Curvature-aware Geometric Black-box Attack
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A. Rahmati
Tianfu Wu
H. Dai
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22
16
0
06 Aug 2023
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation
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Muhammad Abdullah Hanif
B. Ouni
Muhammad Shafique
MDE
40
12
0
06 Aug 2023
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness
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Xing-yuan Chen
Binghai Yan
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26
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0
01 Aug 2023
A Novel Deep Learning based Model to Defend Network Intrusion Detection System against Adversarial Attacks
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Aasim Zafar
Shiekh Burhan Ul Haque
AAML
8
8
0
31 Jul 2023
Theoretically Principled Trade-off for Stateful Defenses against Query-Based Black-Box Attacks
Ashish Hooda
Neal Mangaokar
Ryan Feng
Kassem Fawaz
S. Jha
Atul Prakash
AAML
21
3
0
30 Jul 2023
Adversarial training for tabular data with attack propagation
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Joao Bravo
Marco O. P. Sampaio
Paolo Romano
Hugo Ferreira
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27
1
0
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NSA: Naturalistic Support Artifact to Boost Network Confidence
Abhijith Sharma
Phil Munz
Apurva Narayan
AAML
30
1
0
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Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Dong Lu
Zhiqiang Wang
Teng Wang
Weili Guan
Hongchang Gao
Feng Zheng
AAML
53
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0
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Guopeng Li
Yue Xu
Jian Ding
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A LLM Assisted Exploitation of AI-Guardian
Nicholas Carlini
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Towards Building More Robust Models with Frequency Bias
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Heming Cui
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17
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Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation
Asif Hanif
Muzammal Naseer
Salman Khan
M. Shah
Fahad Shahbaz Khan
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OOD
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4
0
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Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning
Byung-Kwan Lee
Junho Kim
Yonghyun Ro
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30
9
0
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Omnipotent Adversarial Training in the Wild
Guanlin Li
Kangjie Chen
Yuan Xu
Han Qiu
Tianwei Zhang
26
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0
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A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness
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Joshua Andle
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47
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0
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40
4
0
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Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey
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Ivan V. Bajić
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Group-based Robustness: A General Framework for Customized Robustness in the Real World
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Keane Lucas
Neo Eyal
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Michael K. Reiter
Mahmood Sharif
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AAML
33
1
0
29 Jun 2023
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking
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Yuru Su
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AAML
23
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0
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B. Kantarci
Norbert Tihanyi
Lucas C. Cordeiro
Merouane Debbah
Djallel Hamouda
Muna Al-Hawawreh
K. Choo
27
43
0
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Group Orthogonalization Regularization For Vision Models Adaptation and Robustness
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Noga Bar
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32
0
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Maestro: A Gamified Platform for Teaching AI Robustness
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Jiacen Xu
Manikanta Loya
Junlin Wang
Sameer Singh
Zhou Li
Sergio Gago-Masague
19
0
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14 Jun 2023
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Y. Fu
Ye Yuan
Souvik Kundu
Shang Wu
Shunyao Zhang
Yingyan Lin
AAML
68
6
0
10 Jun 2023
Graph-based methods coupled with specific distributional distances for adversarial attack detection
dwight nwaigwe
Lucrezia Carboni
Martial Mermillod
Sophie Achard
M. Dojat
AAML
32
3
0
31 May 2023
Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis
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Iysa Iqbal
M. Hossain
M. A. Karim
Victor A. Clincy
R. Voicu
AAML
26
8
0
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Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness
Zhan Hu
Jun Zhu
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Xiaolin Hu
AAML
29
2
0
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On the Importance of Backbone to the Adversarial Robustness of Object Detectors
Xiao-Li Li
Hang Chen
Xiaolin Hu
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38
4
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Generalizable Synthetic Image Detection via Language-guided Contrastive Learning
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Shile Zhang
118
27
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23 May 2023
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses
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49
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0
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Exploiting Frequency Spectrum of Adversarial Images for General Robustness
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31
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31
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Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature
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Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks
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Evading DeepFake Detectors via Adversarial Statistical Consistency
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34
48
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Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
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