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1511.04599
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DeepFool: a simple and accurate method to fool deep neural networks
14 November 2015
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
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Papers citing
"DeepFool: a simple and accurate method to fool deep neural networks"
50 / 2,298 papers shown
Title
Cross-Modality Attack Boosted by Gradient-Evolutionary Multiform Optimization
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Zhenzhong Wang
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54
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Yufei Song
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Dezhong Yao
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91
12
0
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Showing Many Labels in Multi-label Classification Models: An Empirical Study of Adversarial Examples
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Wenjian Luo
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23
0
0
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The Roles of Generative Artificial Intelligence in Internet of Electric Vehicles
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Dusit Niyato
Wei Zhang
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Hongyang Du
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70
2
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ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer
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Haining Wang
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85
1
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20 Sep 2024
Hidden Activations Are Not Enough: A General Approach to Neural Network Predictions
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Aiky Rasolomanana
Marco Armenta
74
0
0
20 Sep 2024
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification
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Sheetal Kalyani
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43
0
0
17 Sep 2024
Real-world Adversarial Defense against Patch Attacks based on Diffusion Model
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Caixin Kang
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Ziyi Wang
Shouwei Ruan
Yubo Chen
Hang Su
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69
3
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A Cost-Aware Approach to Adversarial Robustness in Neural Networks
Charles Meyers
Mohammad Reza Saleh Sedghpour
Tommy Löfstedt
Erik Elmroth
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71
0
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Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models
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Junming Hou
Jie Gui
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James Tin-Yau Kwok
Yuan Yan Tang
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58
0
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Input Space Mode Connectivity in Deep Neural Networks
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Ori Shem-Ur
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110
1
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163
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A practical approach to evaluating the adversarial distance for machine learning classifiers
Georg Siedel
Ekagra Gupta
Andrey Morozov
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64
0
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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks
Akshay Jain
S. Dubey
Satish Kumar Singh
KC Santosh
B. B. Chaudhuri
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65
0
0
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One-Index Vector Quantization Based Adversarial Attack on Image Classification
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Xiaona Qin
Shuang Chen
Hubert P. H. Shum
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51
0
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Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks
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V. Snás̃el
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64
1
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Statistical Challenges with Dataset Construction: Why You Will Never Have Enough Images
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47
1
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Iterative Window Mean Filter: Thwarting Diffusion-based Adversarial Purification
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Ruoxi Sun
Cunjian Chen
Minhui Xue
Lay-Ki Soon
Shuo Wang
Zhe Jin
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92
2
0
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Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis
Kira Maag
Roman Resner
Asja Fischer
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106
0
0
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Attack Anything: Blind DNNs via Universal Background Adversarial Attack
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Shaohui Mei
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Mingyang Ma
Lap-Pui Chau
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78
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0
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Exploring Cross-model Neuronal Correlations in the Context of Predicting Model Performance and Generalizability
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Lionel Levine
Majid Sarrafzadeh
37
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Massimo Tistarelli
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137
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PixelFade: Privacy-preserving Person Re-identification with Noise-guided Progressive Replacement
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90
1
0
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38
0
0
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Wen Jiang
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58
4
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Label Augmentation for Neural Networks Robustness
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85
1
0
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Joint Universal Adversarial Perturbations with Interpretations
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70
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173
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106
1
0
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Enhancing Adversarial Text Attacks on BERT Models with Projected Gradient Descent
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From ML to LLM: Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks
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123
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Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective
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73
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Backdoor Attacks against Image-to-Image Networks
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141
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Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
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Data Poisoning Attacks in Intelligent Transportation Systems: A Survey
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