Learning to Learn Transferable Attack
Towards Transferable Adversarial Attacks on Vision Transformers
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks
Shape Prior Guided Attack: Sparser Perturbations on 3D Point Clouds
Adversarial Attack for Asynchronous Event-Based Data
CLPA: Clean-Label Poisoning Availability Attacks Using Generative Adversarial Nets
TextHoaxer: Budgeted Hard-Label Adversarial Attacks on Text
Hibernated Backdoor: A Mutual Information Empowered Backdoor Attack to Deep Neural Networks
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning
Attacking Video Recognition Models with Bullet-Screen Comments
Context-Aware Transfer Attacks for Object Detection
A Fusion-Denoising Attack on InstaHide with Data Augmentation
FCA: Learning a 3D Full-Coverage Vehicle Camouflage for Multi-View Physical Adversarial Attack
Backdoor Attacks on the DNN Interpretation System
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
Synthetic Disinformation Attacks on Automated Fact Verification Systems
Adversarial Bone Length Attack on Action Recognition
Improved Gradient Based Adversarial Attacks for Quantized Networks
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification
Has CEO Gender Bias Really Been Fixed? Adversarial Attacking and Improving Gender Fairness in Image Search
Boosting the Transferability of Video Adversarial Examples via Temporal Translation
Learning Universal Adversarial Perturbation by Adversarial Example
Making Adversarial Examples More Transferable and Indistinguishable
Vision Transformers are Robust Learners
Certified Robustness of Nearest Neighbors Against Data Poisoning and Backdoor Attacks
Preemptive Image Robustification for Protecting Users Against Man-in-the-Middle Adversarial Attacks
Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs
When Can the Defender Effectively Deceive Attackers in Security Games?
Robust Heterogeneous Graph Neural Networks against Adversarial Attacks
Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation
Consistency Regularization for Adversarial Robustness
Adversarial Robustness in Multi-Task Learning: Promises and Illusions
LogicDef: An Interpretable Defense Framework Against Adversarial Examples via Inductive Scene Graph Reasoning
Efficient Robust Training via Backward Smoothing
Input-Specific Robustness Certification for Randomized Smoothing
CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks
Adversarial Texture for Fooling Person Detectors in the Physical World
Adversarial Eigen Attack on Black-Box Models
Bounded Adversarial Attack on Deep Content Features
Backdoor Attacks on Self-Supervised Learning
Bandits for Structure Perturbation-Based Black-Box Attacks To Graph Neural Networks With Theoretical Guarantees
Boosting Black-Box Attack With Partially Transferred Conditional Adversarial Distribution
BppAttack: Stealthy and Efficient Trojan Attacks Against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning
Cross-Modal Transferable Adversarial Attacks From Images to Videos
Can You Spot the Chameleon? Adversarially Camouflaging Images From Co-Salient Object Detection
DTA: Physical Camouflage Attacks using Differentiable Transformation Network
DST: Dynamic Substitute Training for Data-Free Black-Box Attack
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution
DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors
Exploring Effective Data for Surrogate Training Towards Black-Box Attack
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
Fairness-Aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models
FIBA: Frequency-Injection Based Backdoor Attack in Medical Image Analysis
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
Shape-Invariant 3D Adversarial Point Clouds
Stereoscopic Universal Perturbations Across Different Architectures and Datasets
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer
Label-Only Model Inversion Attacks via Boundary Repulsion
Improving Adversarial Transferability via Neuron Attribution-Based Attacks
Improving the Transferability of Targeted Adversarial Examples Through Object-Based Diverse Input
Investigating Top-k White-Box and Transferable Black-Box Attack
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network
Zero-Query Transfer Attacks on Context-Aware Object Detectors
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free
Towards Efficient Data Free Blackbox Adversarial Attack
Transferable Sparse Adversarial Attack
Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks
DEFEAT: Deep Hidden Feature Backdoor Attacks by Imperceptible Perturbation and Latent Representation Constraints
Exploring Frequency Adversarial Attacks for Face Forgery Detection
360-Attack: Distortion-Aware Perturbations From Perspective-Views
-
defense
Enhancing Adversarial Training With Second-Order Statistics of Weights
Enhancing Adversarial Robustness for Deep Metric Learning
Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching
Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations
Subspace Adversarial Training
Segment and Complete: Defending Object Detectors Against Adversarial Patch Attacks With Robust Patch Detection
Self-Supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Towards Practical Certifiable Patch Defense with Vision Transformer
Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack
LAS-AT: Adversarial Training with Learnable Attack Strategy
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks With Implicit Gradients
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Defensive Patches for Robust Recognition in the Physical World
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training
On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles
EyePAD++: A Distillation-Based Approach for Joint Eye Authentication and Presentation Attack Detection Using Periocular Images
-
其他
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart
Robust Combination of Distributed Gradients Under Adversarial Perturbations
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation
Leveraging Adversarial Examples To Quantify Membership Information Leakage
Imitated Detectors: Stealing Knowledge of Black-box Object Detectors
Generating Transferable Adversarial Examples against Vision Transformers
-
attack
Frequency Domain Model Augmentation for Adversarial Attack
Adversarially-Aware Robust Object Detector
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow
Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches
Shape Matters: Deformable Patch Attack
LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Adaptive Image Transformations for Transfer-based Adversarial Attack
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction
Triangle Attack: A Query-efficient Decision-based Adversarial Attack
Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation
Exploiting the local parabolic landscapes of adversarial losses to accelerate black-box adversarial attack
A Large-scale Multiple-objective Method for Black-box Attack against Object Detection
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
GradAuto: Energy-oriented Attack on Dynamic Neural Networks
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness
TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations
Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack
Improving Robustness by Enhancing Weak Subnets
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Prior-Guided Adversarial Initialization for Fast Adversarial Training
Enhanced Accuracy and Robustness via Multi-Teacher Adversarial Distillation
Learning Robust and Lightweight Model through Separable Structured Transformations
All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines
Robustness and Beyond: Unleashing Efficient Adversarial Training
One Size Does NOT Fit All: Data-Adaptive Adversarial Training
Revisiting Outer Optimization in Adversarial Training
Scaling Adversarial Training to Large Perturbation Bounds
ViP: Unified Certified Detection and Recovery for Patch Attack with Vision Transformers
Effective Presentation Attack Detection Driven by Face Related Task
Adversarially-Aware Robust Object Detector
Towards Efficient Adversarial Training on Vision Transformers
Revisiting Outer Optimization in Adversarial Training
RIBAC: Towards Robust and Imperceptible Backdoor Attack against Compact DNN
An Invisible Black-box Backdoor Attack through Frequency Domain
Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks
Hardly Perceptible Trojan Attack against Neural Networks with Bit Flips
Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning
Zero-Shot Attribute Attacks on Fine-Grained Recognition Models
An Impartial Take to the CNN vs Transformer Robustness Contest
ICLR 2022 Conference | OpenReview
On Improving Adversarial Transferability of Vision Transformers
Online Adversarial Attacks
Attacking deep networks with surrogate-based adversarial black-box methods is easy
Rethinking Adversarial Transferability from a Data Distribution Perspective
Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models
Data Poisoning Won’t Save You From Facial Recognition
Transferable Adversarial Attack based on Integrated Gradients
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
Provably Robust Adversarial Examples
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Reverse Engineering of Imperceptible Adversarial Image Perturbations
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks
Towards Evaluating the Robustness of Neural Networks Learned by Transduction
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
Backdoor Defense via Decoupling the Training Process
Adversarial Unlearning of Backdoors via Implicit Hypergradient
Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent
Towards Understanding the Robustness Against Evasion Attack on Categorical Data
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations
AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis
Adversarial Robustness Through the Lens of Causality
Fast AdvProp
Self-ensemble Adversarial Training for Improved Robustness
Trigger Hunting with a Topological Prior for Trojan Detection
Provably Robust Adversarial Examples
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
On the Certified Robustness for Ensemble Models and Beyond
Defending Against Image Corruptions Through Adversarial Augmentations
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
On the Convergence of Certified Robust Training with Interval Bound Propagation
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100
Exploring Memorization in Adversarial Training
Adversarially Robust Conformal Prediction
NeurIPS 2022
-
attack
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks
GAMA: Generative Adversarial Multi-Object Scene Attacks
BadPrompt: Backdoor Attacks on Continuous Prompts
VoiceBox: Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias
Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
Revisiting Injective Attacks on Recommender Systems
Perceptual Attacks of No-Reference lmage Quality Models with Human-in-the-Loop
Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class
Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation
Adv-Attribute: lnconspicuous and Transferable Adversarial Attack on Face Recognition
Black box Attacks via Surrogate Ensemble Search
Natural Color Fool : Towards Boosting Black-box Unrestricted Attacks
Towards Lightweight Black-Box Attack Against Deep Neural Networks
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
One-shot Neural Backdoor Erasing via Adversarial Weight Masking Pre-trained Adversarial Perturbations
lsometric 3D Adversarial Examples in the Physical World
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition
MORA: Improving Ensemble Robustness Evaluation with Model Reweighing Attack
Adversarial Robustness is at Odds with Lazy Training
Defending Against Adversarial Attacks via Neural Dynamic System
A2: Efficient Automated Attacker for Boosting Adversarial Training
Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
Formulating Robustness Against Unforeseen Attacks
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC
Adversarial training for high-stakes reliability
Phase Transition from Clean Training to Adversarial Training
Why Do Artificially Generated Data Help Adversarial Robustness
Toward Robust Spiking Neural Network Against Adversarial Perturbation
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
SNN-RAT:Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training
A CloserLook at the Adversarial Robustness of Deep Equilibrium Models
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
CalFAT: Calibrated Federated Adversarial Training with Label Skewness
Enhance the Visual Representation via Discrete Adversarial Training
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfittingview
Adversarialy Robust Learning: A Generic Minimax Optimal Learner and Characterization
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
Stability Analysis and Generalization Bounds of Adversarial Training
Efficient and Effective Augmentation Strategy for Adversarial Training
lmproving Adversarial Robustness of Vision Transformers
Random Normalization Aggregation for Adversarial Defense
DISCo: Adversarial Defense with Local lmplicit Functions
Synergy-of-Experts : Collaborate to Improve Adversarial Robustness
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Rethinking Lipschitz Neural Networks for Certified L-infinity Robustness
-
其他
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Can AdversarialTraining Be Manipulated By Non-Robust Features?
A Characterization of Semi-Supervised Adversarially Robust PAC Learnability
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
On the Adversarial Robustness of Mixture of Experts
Increasing Confidence in Adversarial Robustness Evaluations
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?
后续会持续更新 2022 -2023年看过的论文
[
AAAI
2021]
Deep
Robust 是一个 PyTorch
对抗
库，用于对图像和图形进行
攻击
和
防御
方法。
如果您是
Deep
Robust 的新手，我们强烈建议您阅读本 README 中的或以下内容以了解如何使用它。
如果您对此库有任何问题或建议，请随时创建问题。 我们会尽快回复 ：）
包含算法的列表可以在和。
关于攻防的更多细节，可以阅读以下论文。
图的
对抗
性
攻击
和
防御
：综述、工具和实证研究
图像、图形和文本中的
对抗
性
攻击
和
防御
：综述
如果我们的工作对您的研究有帮助，请引用：
Deep
Robust: A PyTorch Library for
Adve
rsa
ria
l
Att
acks
and
Def
enses
@article{li2020
deep
robust,
title={
Deep
robust: A pytorch lib
