Make fawkes builds and dependencies reproducible and easy to maintain

.python-version

@@ -0,0 +1 @@
+3.9.0

README.md

@@ -18,6 +18,25 @@ This code is intended only for personal privacy protection or academic research.
 Usage
 -----
 
+### Local Development (Most reliable and future-proof method of running fawkes)
+
+```commandline
+# Install pyenv before running these commands
+# Instructions to install pyenv vary per OS.
+pyenv install 3.9.0
+pyenv global 3.9.0
+pip install poetry 
+# After this point global python can be anything
+# the fawkes repos .python-version file tells pyenv to use 3.9.0 when inside the fawkes folder.
+
+pip install poetry # Install poetry
+
+pyenv install 3.9.0
+cd fawkes
+poetry install
+poetry run python .\fawkes\protection.py -d ./imgs --mode low
+```
+
 `$ fawkes`
 
 Options:
@@ -34,9 +53,6 @@ Options:
 
 `fawkes -d ./imgs --mode low`
 
-or `python3 protection.py -d ./imgs --mode low`
-
-
 ### Tips
 
 - The perturbation generation takes ~60 seconds per image on a CPU machine, and it would be much faster on a GPU

fawkes/differentiator.py

@@ -195,7 +195,7 @@ def compute_batch(self, source_imgs, target_imgs=None, retry=True):
                                     dtype=tf.float32)
 
         # make the optimizer
-        optimizer = tf.keras.optimizers.Adadelta(float(self.learning_rate))
+        optimizer = tf.keras.optimizers.legacy.Adadelta(float(self.learning_rate))
         const_numpy = np.ones(len(source_imgs)) * self.initial_const
         self.const = tf.Variable(const_numpy, dtype=np.float32)
 

fawkes/protection.py

@@ -75,7 +75,7 @@ def mode2param(self, mode):
             extractors = ["extractor_0", "extractor_2"]
 
         else:
-            raise Exception("mode must be one of 'min', 'low', 'mid', 'high'")
+            raise Exception("mode must be one of 'low', 'mid', 'high'")
         return th, max_step, lr, extractors
 
     def run_protection(self, image_paths, th=0.04, sd=1e7, lr=10, max_step=500, batch_size=1, format='png',

fawkes/utils.py

@@ -35,7 +35,7 @@
 from PIL import Image, ExifTags
 from keras.layers import Dense, Activation
 from keras.models import Model
-from keras.preprocessing import image
+from keras.utils import image_utils as image
 
 from fawkes.align_face import align
 from six.moves.urllib.request import urlopen
@@ -263,7 +263,7 @@ def load_victim_model(number_classes, teacher_model=None, end2end=False):
 
 def resize(img, sz):
     assert np.min(img) >= 0 and np.max(img) <= 255.0
-    from keras.preprocessing import image
+    from keras.utils import image_utils as image
     im_data = image.array_to_img(img).resize((sz[1], sz[0]))
     im_data = image.img_to_array(im_data)
     return im_data

publish.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+# Publish the package
+poetry publish --build
+
+# Get the current version
+VERSION=$(poetry version -s)
+
+# Create and push a Git tag
+git tag v$VERSION
+git push origin v$VERSION

pyproject.toml

@@ -0,0 +1,48 @@
+[project]
+name = "fawkes"
+version = "0.1.0"
+description = ""
+authors = [
+    {name = "Shawn Shan", email = "[email protected]"}
+]
+license = "BSD"
+homepage = "https://github.com/Shawn-Shan/fawkes"
+keywords = ["fawkes", "privacy", "ML"]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Topic :: System :: Monitoring",
+]
+
+readme = "README.md"
+requires-python = "~=3.9"
+dependencies = [
+    "numpy (>=1.22,<2.0)",
+    "tensorflow-io-gcs-filesystem (>=0.23.1,<0.24.0)",
+    "tensorflow (==2.12)",
+    "keras (>=2.12.0,<2.13.0)",
+    "setuptools (>=75.8.0,<76.0.0)",
+    "mtcnn (>=0.1.0,<0.2.0)",
+    "pillow (>=11.1.0,<12.0.0)",
+    "bleach (>=6.2.0,<7.0.0)",
+    "pyqt5 (==5.15.2)",
+    # Despite the name amd runs this implementation fine.
+    # There's no built in poetry env variable that'll tell us if an AMD GPU is present (which would imply tensorflow-rocm could be a more efficient implementation).
+    # A script could probably be built that intelligently runs pip install under the hood but this is hard to track for a newbie.
+    "tensorflow-intel (==2.12.0)"
+
+]
+
+
+[tool.poetry.dependencies]
+python = "~3.9"
+
+[tool.poetry.scripts]
+fawkes = "fawkes:main"
+
+
+[build-system]
+requires = ["poetry-core>=2.0.0,<3.0.0"]
+build-backend = "poetry.core.masonry.api"