AAAI2022对抗攻击&防御文章汇总
攻击
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
防御
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
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