!158 Update release.md to v1.1.0

From: @pkuliuliu
Reviewed-by: @liu_luobin,@jxlang910
Signed-off-by: @jxlang910

RELEASE.md

@@ -1,3 +1,51 @@
+# MindArmour 1.1.0 Release Notes
+
+## MindArmour
+
+### Major Features and Improvements
+
+* [STABLE] Attack capability of the Object Detection models.
+    * Some white-box adversarial attacks, such as [iterative] gradient method and DeepFool now can be applied to Object Detection models.
+    * Some black-box adversarial attacks, such as PSO and Genetic Attack now can be applied to Object Detection models.
+
+### Backwards Incompatible Change
+
+#### Python API
+
+#### C++ API
+
+### Deprecations
+
+#### Python API
+
+#### C++ API
+
+### New Features
+
+#### Python API
+
+#### C++ API
+
+### Improvements
+
+#### Python API
+
+#### C++ API
+
+### Bug fixes
+
+#### Python API
+
+#### C++ API
+
+## Contributors
+
+Thanks goes to these wonderful people:
+
+Xiulang Jin, Zhidan Liu, Luobin Liu and Liu Liu.
+
+Contributions of any kind are welcome!
+
 # Release 1.0.0
 
 ## Major Features and Improvements
@@ -16,9 +64,11 @@
     * Parameter verification enhancement.
 
 ### Other
+
 * Api & Directory Structure
     * Adjusted the directory structure based on different features.
     * Optimize the structure of examples.
+
 ## Bugfixes
 
 ## Contributors
@@ -29,7 +79,6 @@ Liu Liu, Xiulang Jin, Zhidan Liu and Luobin Liu.
 
 Contributions of any kind are welcome!
 
-
 # Release 0.7.0-beta
 
 ## Major Features and Improvements
@@ -38,13 +87,13 @@ Contributions of any kind are welcome!
 
 * Privacy leakage evaluation.
 
-  * Using Membership inference to evaluate the effectiveness of privacy-preserving techniques for AI.
+    * Using Membership inference to evaluate the effectiveness of privacy-preserving techniques for AI.
 
 ### Model robustness evaluation
 
 * Fuzzing based Adversarial Robustness testing.
 
-  * Coverage-guided test set generation.
+    * Coverage-guided test set generation.
 
 ## Bugfixes
 
@@ -56,7 +105,6 @@ Liu Liu, Xiulang Jin, Zhidan Liu, Luobin Liu and Huanhuan Zheng.
 
 Contributions of any kind are welcome!
 
-
 # Release 0.6.0-beta
 
 ## Major Features and Improvements
@@ -65,15 +113,15 @@ Contributions of any kind are welcome!
 
 * Optimizers with differential privacy
 
-  * Differential privacy model training now supports some new policies.
+    * Differential privacy model training now supports some new policies.
 
-  * Adaptive Norm policy is supported.
+    * Adaptive Norm policy is supported.
 
-  * Adaptive Noise policy with exponential decrease is supported.  
+    * Adaptive Noise policy with exponential decrease is supported.  
 
 * Differential Privacy Training Monitor
 
-  * A new monitor is supported using zCDP as its asymptotic budget estimator.
+    * A new monitor is supported using zCDP as its asymptotic budget estimator.
 
 ## Bugfixes
 
@@ -85,7 +133,6 @@ Liu Liu, Huanhuan Zheng, XiuLang jin, Zhidan liu.
 
 Contributions of any kind are welcome.
 
-
 # Release 0.5.0-beta
 
 ## Major Features and Improvements
@@ -108,7 +155,6 @@ Liu Liu, Huanhuan Zheng, Xiulang Jin, Zhidan Liu.
 
 Contributions of any kind are welcome!
 
-
 # Release 0.3.0-alpha
 
 ## Major Features and Improvements
@@ -117,29 +163,39 @@ Contributions of any kind are welcome!
 
 Differential Privacy is coming! By using Differential-Privacy-Optimizers, one can still train a model as usual, while the trained model preserved the privacy of training dataset, satisfying the definition of
 differential privacy with proper budget.
+
 * Optimizers with Differential Privacy([PR23](https://gitee.com/mindspore/mindarmour/pulls/23), [PR24](https://gitee.com/mindspore/mindarmour/pulls/24))
-    * Some common optimizers now have a differential privacy version (SGD/
-    Adam). We are adding more.
+
+    * Some common optimizers now have a differential privacy version (SGD/Adam). We are adding more.
     * Automatically and adaptively add Gaussian Noise during training to achieve Differential Privacy.
     * Automatically stop training when Differential Privacy Budget exceeds.
+
 * Differential Privacy Monitor([PR22](https://gitee.com/mindspore/mindarmour/pulls/22))
+
     * Calculate overall budget consumed during training, indicating the ultimate protect effect.
+
 ## Bug fixes
+
 ## Contributors
-Thanks goes to these wonderful people: 
+
+Thanks goes to these wonderful people:
 Liu Liu, Huanhuan Zheng, Zhidan Liu, Xiulang Jin
 Contributions of any kind are welcome!
 
 # Release 0.2.0-alpha
+
 ## Major Features and Improvements
-- Add a white-box attack method: M-DI2-FGSM([PR14](https://gitee.com/mindspore/mindarmour/pulls/14)).
-- Add three neuron coverage metrics: KMNCov, NBCov, SNACov([PR12](https://gitee.com/mindspore/mindarmour/pulls/12)).
-- Add a coverage-guided fuzzing test framework for deep neural networks([PR13](https://gitee.com/mindspore/mindarmour/pulls/13)).
-- Update the MNIST Lenet5 examples.
-- Remove some duplicate code.
+
+* Add a white-box attack method: M-DI2-FGSM([PR14](https://gitee.com/mindspore/mindarmour/pulls/14)).
+* Add three neuron coverage metrics: KMNCov, NBCov, SNACov([PR12](https://gitee.com/mindspore/mindarmour/pulls/12)).
+* Add a coverage-guided fuzzing test framework for deep neural networks([PR13](https://gitee.com/mindspore/mindarmour/pulls/13)).
+* Update the MNIST Lenet5 examples.
+* Remove some duplicate code.
 
 ## Bug fixes
+
 ## Contributors
+
 Thanks goes to these wonderful people:
 Liu Liu, Huanhuan Zheng, Zhidan Liu, Xiulang Jin
 Contributions of any kind are welcome!
@@ -150,8 +206,8 @@ Initial release of MindArmour.
 
 ## Major Features
 
-- Support adversarial attack and defense on the platform of MindSpore.
-- Include 13 white-box and 7 black-box attack methods.
-- Provide 5 detection algorithms to detect attacking in multiple way.
-- Provide adversarial training to enhance model security.
-- Provide 6 evaluation metrics for attack methods and 9 evaluation metrics for defense methods.
+* Support adversarial attack and defense on the platform of MindSpore.
+* Include 13 white-box and 7 black-box attack methods.
+* Provide 5 detection algorithms to detect attacking in multiple way.
+* Provide adversarial training to enhance model security.
+* Provide 6 evaluation metrics for attack methods and 9 evaluation metrics for defense methods.