From 5b256ce63cf132b135be197620c606653a2bfe8b Mon Sep 17 00:00:00 2001
From: Yiming Li <46520010+THUYimingLi@users.noreply.github.com>
Date: Tue, 21 Jun 2022 14:14:23 +0800
Subject: [PATCH] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index e21ea0d..f057ec9 100644
--- a/README.md
+++ b/README.md
@@ -41,8 +41,8 @@ Currently, this toolbox is still under development (but the attack parts are alm
 |:---------------------------------------------------------------------------------------------------------------:|:---------------------:|------------------------------------------------------------|-------------------------------------------------------------|
 |             [ShrinkPad](https://github.com/THUYimingLi/BackdoorBox/blob/main/core/defenses/ShrinkPad.py)             |   [Backdoor Attack in the Physical World](https://arxiv.org/pdf/2104.02361.pdf). ICLR Workshop, 2021.    | Sample Pre-processing                                                | efficient defense                                     |
 |          [FineTuning](https://github.com/THUYimingLi/BackdoorBox/blob/main/core/defenses/FineTuning.py)         |      [Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks](https://arxiv.org/pdf/1805.12185.pdf). RAID, 2018.     | Model Repairing                                     | first defense based on model repairing                                      |
-|          [MCR](https://github.com/THUYimingLi/BackdoorBox/blob/main/core/defenses/MCR.py)         |      [Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness](https://arxiv.org/pdf/2005.00060.pdf). ICLR, 2020     | Model Repairing                                     |                                     |
-|          [NAD](https://github.com/THUYimingLi/BackdoorBox/blob/main/core/defenses/NAD.py)         |      [Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks](https://openreview.net/pdf?id=9l0K4OM-oXE). ICLR, 2021     | Model Repairing                                     |  first distillation-based defense                                   |
+|          [MCR](https://github.com/THUYimingLi/BackdoorBox/blob/main/core/defenses/MCR.py)         |      [Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness](https://arxiv.org/pdf/2005.00060.pdf). ICLR, 2020.     | Model Repairing                                     |                                     |
+|          [NAD](https://github.com/THUYimingLi/BackdoorBox/blob/main/core/defenses/NAD.py)         |      Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks. [ICLR]((https://openreview.net/pdf?id=9l0K4OM-oXE)), 2021.     | Model Repairing                                     |  first distillation-based defense                                   |