科研篇(12)：CVPR20 分类整理-对抗样本
文章目录
- 一、对抗样本-附代码
- 1.1Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance.
- 1.2 One Man's Trash Is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples
- 1.3 ColorFool: Semantic Adversarial Colorization
- 1.4 Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking
- 1.5 Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
- 1.6 Efficient Adversarial Training with Transferable Adversarial Examples
- 1.7 Modeling Biological Immunity to Adversarial Examples
- 1.8 Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
- 1.9 (Oral)A Self-supervised Approach for Adversarial Robustness
- 1.10 When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
- 1.11 Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder
- 1.12 Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
- 1.13 Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
- 1.14 LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
- 二、对抗样本-无代码
- 2.1Polishing Decision-Based Adversarial Noise With a Customized Sampling.
- 2.2 Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations
- 2.3 Single-Step Adversarial Training With Dropout Scheduling.
- 2.4 Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
- 2.5 Boosting the Transferability of Adversarial Samples via Attention
- 2.6 Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness
- 2.7 On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks
- 2.8 Adversarial Examples Improve Image Recognition
- 2.9 Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction
- 2.10 Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles
- 2.11 Benchmarking Adversarial Robustness on Image Classification
- 2.11 DaST: Data-Free Substitute Training for Adversarial Attacks
- 2.12 Ensemble Generative Cleaning With Feedback Loops for Defending Adversarial Attacks
- 2.13 Exploiting Joint Robustness to Adversarial Perturbations
- 2.14 GeoDA: A Geometric Framework for Black-Box Adversarial Attacks
- 2.15 What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images
- 2.16 Physically Realizable Adversarial Examples for LiDAR Object Detection
- 2.17 One-Shot Adversarial Attacks on Visual Tracking With Dual Attention
- 2.18 Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack
- 2.19 Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations
- 2.20 Robust Superpixel-Guided Attentional Adversarial Attack
- 2.21 ILFO: Adversarial Attack on Adaptive Neural Networks
- 2.22 PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving
- 2.23 Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
一、对抗样本-附代码
1.1Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance.
PAPER LINK
CODE
1.2 One Man’s Trash Is Another Man’s Treasure: Resisting Adversarial Examples by Adversarial Examples
PAPER LINK
CODE
1.3 ColorFool: Semantic Adversarial Colorization
PAPER LINK
CODE
1.4 Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking
PAPER LINK
CODE
1.5 Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
PAPER LINK
CODE
1.6 Efficient Adversarial Training with Transferable Adversarial Examples
PAPER LINK
CODE
1.7 Modeling Biological Immunity to Adversarial Examples
PAPER LINK
CODE
1.8 Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
PAPER LINK
CODE
1.9 (Oral)A Self-supervised Approach for Adversarial Robustness
PAPER LINK
CODE
1.10 When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
PAPER LINK
CODE
1.11 Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder
PAPER LINK
CODE
1.12 Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory
PAPER LINK
CODE
1.13 Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
PAPER LINK
CODE
1.14 LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
PAPER LINK
CODE
二、对抗样本-无代码
2.1Polishing Decision-Based Adversarial Noise With a Customized Sampling.
PAPER LINK
2.2 Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations
PAPER LINK
2.3 Single-Step Adversarial Training With Dropout Scheduling.
PAPER LINK
2.4 Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
PAPER LINK
2.5 Boosting the Transferability of Adversarial Samples via Attention
PAPER LINK
2.6 Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness
PAPER LINK
2.7 On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks
PAPER LINK
2.8 Adversarial Examples Improve Image Recognition
PAPER LINK
2.9 Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction
PAPER LINK
2.10 Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles
PAPER LINK
2.11 Benchmarking Adversarial Robustness on Image Classification
PAPER LINK
2.11 DaST: Data-Free Substitute Training for Adversarial Attacks
PAPER LINK
2.12 Ensemble Generative Cleaning With Feedback Loops for Defending Adversarial Attacks
PAPER LINK
2.13 Exploiting Joint Robustness to Adversarial Perturbations
PAPER LINK
2.14 GeoDA: A Geometric Framework for Black-Box Adversarial Attacks
PAPER LINK
2.15 What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images
PAPER LINK
2.16 Physically Realizable Adversarial Examples for LiDAR Object Detection
PAPER LINK
2.17 One-Shot Adversarial Attacks on Visual Tracking With Dual Attention
PAPER LINK
2.18 Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack
PAPER LINK
2.19 Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations
PAPER LINK
2.20 Robust Superpixel-Guided Attentional Adversarial Attack
PAPER LINK
2.21 ILFO: Adversarial Attack on Adaptive Neural Networks
PAPER LINK
2.22 PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving
PAPER LINK
2.23 Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
PAPER LINK