https://github.com/cuge1995/iccv-2021-adversarial-attacks-and-defense
ICCV 2021 papers and code focus on adversarial attacks and defense
https://github.com/cuge1995/iccv-2021-adversarial-attacks-and-defense
adversarial-attacks deep-learning defense-methods iccv2021
Last synced: 3 months ago
JSON representation
ICCV 2021 papers and code focus on adversarial attacks and defense
- Host: GitHub
- URL: https://github.com/cuge1995/iccv-2021-adversarial-attacks-and-defense
- Owner: cuge1995
- Created: 2021-11-02T08:31:57.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-11-05T02:38:26.000Z (over 3 years ago)
- Last Synced: 2025-01-12T22:33:01.351Z (5 months ago)
- Topics: adversarial-attacks, deep-learning, defense-methods, iccv2021
- Homepage:
- Size: 40 KB
- Stars: 11
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ICCV-2021-adversarial-attacks-and-defense
ICCV 2021 papers and code focus on adversarial attacks and defense
## Attacks
### clssification
* AdvDrop: Adversarial Attack to DNNs by Dropping Information
* Admix: Enhancing the Transferability of Adversarial Attacks
* Feature Importance-Aware Transferable Adversarial Attacks
* Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces
* Augmented Lagrangian Adversarial Attacks
* [code](https://github.com/jeromerony/augmented_lagrangian_adversarial_attacks)
* LIRA: Learnable, Imperceptible and Robust Backdoor Attacks
* Interpreting Attributions and Interactions of Adversarial Attacks
### detection
### point cloud
* PointBA: Towards Backdoor Attacks in 3D Point Cloud
* A Backdoor Attack Against 3D Point Cloud Classifiers
* Meta Gradient Adversarial Attack
### other tasks
* Adversarial Attack on Deep Cross-Modal Hamming Retrieval `Hamming Retrieval`
* Just One Moment: Structural Vulnerability of Deep Action Recognition Against One Frame Attack `Action Recognition`
* Parallel Rectangle Flip Attack: A Query-Based Black-Box Attack Against Object Detection `Object Detection`
* Practical Relative Order Attack in Deep Ranking `Ranking`
* Adversarial Attacks on Multi-Agent Communication
* Membership Inference Attacks Are Easier on Difficult Problems `Membership Inference Attacks`
* Knowledge-Enriched Distributional Model Inversion Attacks `Model Inversion Attacks`
* [code]( https://github.com/SCccc21/Knowledge-Enriched-DMI)
* Exploiting Explanations for Model Inversion Attacks `Model Inversion Attacks`
* Aha! Adaptive History-Driven Attack for Decision-Based Black-Box Models
* TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning `Top-k Multi-Label Learning`
* [code](https://github.com/discovershu/TKML-AP)
* Data-Free Universal Adversarial Perturbation and Black-Box Attack
* Attack As the Best Defense: Nullifying Image-to-Image Translation GANs via Limit-Aware Adversarial Attack
* Invisible Backdoor Attack With Sample-Specific Triggers
* Meta-Attack: Class-Agnostic and Model-Agnostic Physical Adversarial Attack
* Attack-Guided Perceptual Data Generation for Real-World Re-Identification
* AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-Directional Metric Learning
* ProFlip: Targeted Trojan Attack With Progressive Bit Flips
## Defense
### Detection adv
* Multi-Expert Adversarial Attack Detection in Person Re-Identification Using Context Inconsistency
* Black-Box Detection of Backdoor Attacks With Limited Information and Data
* Adversarial Attacks Are Reversible With Natural Supervision
* Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective
* [code](https://github.com/YiZeng623/frequency-backdoor)
* Sample Efficient Detection and Classification of Adversarial Attacks via Self-Supervised Embeddings `exp`
* Detection and Continual Learning of Novel Face Presentation Attacks
* Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes
### Adv train
* Improving Robustness of Facial Landmark Detection by Defending Against Adversarial Attacks
* [code]( https://github.com/zhuccly/SAAT)
## Applications
* Triggering Failures: Out-of-Distribution Detection by Learning From Local Adversarial Attacks in Semantic Segmentation