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科研篇(12)：CVPR20 分类整理-对抗样本_understanding adversarial examples from the mutua

作者：从前慢现在也慢 | 2024-07-26 07:06:21

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understanding adversarial examples from the mutual influence of images and

文章目录

一、对抗样本-附代码
二、对抗样本-无代码

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

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