学习笔记：【VALSE短教程】《Adversarial Attack and Defense》
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1、White-box attacks
Direction I
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EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES
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ADVERSARIAL EXAMPLES IN THE PHYSICAL WORLD
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Towards Deep Learning Models Resistant to Adversarial Attacks
Direction II
分错条件下找到扰动最小的对抗样本。
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DeepFool: a simple and accurate method to fool deep neural networks
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Towards Evaluating the Robustness of Neural Networks
2、Black-box Attack
1、Transferability-based Attack
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Boosting Adversarial Attacks with Momentum
NESTEROV ACCELERATED GRADIENT AND SCALE INVARIANCE FOR ADVERSARIAL ATTACKS
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Towards Understanding and Improving the Transferability of Adversarial Examples in Deep Neural Networks
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Improving Transferability of Adversarial Examples with Input Diversity
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SKIP CONNECTIONS MATTER: ON THE TRANSFERABILITY OF ADVERSARIAL EXAMPLES GENERATED WITH RESNETS
2、Query-based Adversarial Attack
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DECISION-BASED ADVERSARIAL ATTACKS: RELIABLE ATTACKS AGAINST BLACK-BOX MACHINE LEARNING MODELS
A Ray Searching Method for Hard-label Adversarial Attack
ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models
Auto ZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks
Square Attack: a query-efficient black-box adversarial attack via random search
Black-box Adversarial Attacks with Limited Queries and Information
N ATTACK:Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks
Improving Query Efficiency of Black-box Adversarial Attack
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Adversarial Patch
Robust Physical-World Attacks on Deep Learning Visual Classification
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Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
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BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
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Towards Deep Learning Models Resistant to Adversarial Attacks
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On the Convergence and Robustness of Adversarial Training
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Adversarial Weight Perturbation HelpsRobust Generalization
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Visualizing the Loss Landscape of Neural Nets
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Understanding Adversarial Robustness Through Loss Landscape Geometries
INTERPRETING ADVERSARIAL ROBUSTNESS:A VIEW FROM DECISION SURFACE IN INPUT SPACE
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Theoretically Principled Trade-off between Robustness and Accuracy
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IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES
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Adversarial Neuron Pruning Purifies Backdoored Deep Models
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Spectral Signatures in Backdoor Attacks
DEEP PARTITION AGGREGATION:PROVABLE DEFENSES AGAINST GENERAL POISONING ATTACKS
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
STRONG DATA AUGMENTATION SANITIZES POISONING AND BACKDOOR ATTACKS WITHOUT AN ACCURACY TRADEOFF
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Data
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Feature Denoising for Improving Adversarial Robustness
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Adversarial Examples Improve Image Recognition
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Improving Adversarial Robustness via Channel-wise Activation Suppressing
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Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability
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Unlearnable Examples: Making Personal Data Unexploitable
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Unadversarial Examples: Designing Objects for Robust Vision
