update readme

Author: 0xEitan

README.md

@@ -1,2 +1,53 @@
 # BANND
-A project for the course "Trustworthy Machine Learning". We investigate defenses for backdoor attacks on neural networks during training.
+
+A project for the course "Trustworthy Machine Learning". We investigate defenses for
+backdoor attacks on neural networks during training.
+
+# Dependencies
+
+The project requires Python 3.10, and dependencies are installed using `pip`.
+
+To install the dependencies, run `python -m pip install -r requirements`.
+
+We recommend using a Virtual Environment for the project. To do that, install
+`virtualenv` with `python -m pip install virtualenv` and create a new virtualenv in the
+project with `python -m virtualenv ./venv`. Then, activate the virtualenv with `source
+./venv/bin/activate` and run all the `python` and `pip` commands within it.
+
+# Running
+
+Run the project with `python bannd.py`. Pass the `-h`/`--help` flag to see all the
+parameters. Note that for brevity's sake, some parameters like the number of epochs and
+batch size are omitted and can't be passed.
+
+We support 3 modes of running:
+
+- Baseline: pass `--runtype baseline` to see the network's baseline accuracy without any
+  attack or defense.
+- Attack: pass `--runtype attack` and `--poison_rate Y` (any other number between
+  `0.01` and `0.99`, we tested with `0.1`, `0.3`, and `0.5`) to run the badnets attack on the network, by poisoning some
+  percentage of the samples with the backdoor. Expect the attack success rate to
+  increase with time.
+- Defense: pass `--runtype defense`, `--poison_rate X`, and `--quantile-threshold Y`
+  (any number between `0.00` and `0.99`, we tested with `0.0`, `0.5`, `0.7`, and `0.85`)
+  to run the attack and defend against it.
+
+## Examples
+
+To run the baseline:
+
+```sh
+python bannd.py --runtype baseline
+```
+
+To run the attack:
+
+```sh
+python bannd.py --runtype attack --poison_rate 0.1
+```
+
+To run the defense:
+
+```sh
+python bannd.py --runtype defense --poison_rate 0.1 --quantile-threshold 0.7
+```