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.gitattributes

@@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/fake_frame_10.png filter=lfs diff=lfs merge=lfs -text
+examples/fake_frame_6.png filter=lfs diff=lfs merge=lfs -text
+examples/fake_frame_7.png filter=lfs diff=lfs merge=lfs -text
+examples/real_frame_1.png filter=lfs diff=lfs merge=lfs -text
+examples/real_frame_13.png filter=lfs diff=lfs merge=lfs -text
+examples/real_frame_19.png filter=lfs diff=lfs merge=lfs -text
+examples/real_frame_20.png filter=lfs diff=lfs merge=lfs -text
+examples/real_frame_3.png filter=lfs diff=lfs merge=lfs -text
+examples/real_frame_8.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,132 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+
+.git
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

Deepfake_detection.ipynb

@@ -0,0 +1,939 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a2220df6",
+ "metadata": {},
+ "source": [
+ "# Import Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "7249bea4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import gradio as gr\n",
+ "import torch\n",
+ "import torch.nn.functional as F\n",
+ "from facenet_pytorch import MTCNN, InceptionResnetV1\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "import cv2\n",
+ "from pytorch_grad_cam import GradCAM\n",
+ "from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget\n",
+ "from pytorch_grad_cam.utils.image import show_cam_on_image\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d25e1c5d",
+ "metadata": {},
+ "source": [
+ "# Download and Load Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "237fbf44",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n",
+ "\n",
+ "mtcnn = MTCNN(\n",
+ " select_largest=False,\n",
+ " post_process=False,\n",
+ " device=DEVICE\n",
+ ").to(DEVICE).eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "f3ef2b4f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "InceptionResnetV1(\n",
+ " (conv2d_1a): BasicConv2d(\n",
+ " (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (conv2d_2a): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (conv2d_2b): BasicConv2d(\n",
+ " (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (maxpool_3a): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " (conv2d_3b): BasicConv2d(\n",
+ " (conv): Conv2d(64, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (conv2d_4a): BasicConv2d(\n",
+ " (conv): Conv2d(80, 192, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (conv2d_4b): BasicConv2d(\n",
+ " (conv): Conv2d(192, 256, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (repeat_1): Sequential(\n",
+ " (0): Block35(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (branch2): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(96, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): Block35(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (branch2): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(96, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): Block35(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (branch2): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(96, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (3): Block35(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (branch2): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(96, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (4): Block35(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (branch2): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(96, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (mixed_6a): Mixed_6a(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 384, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(256, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(192, 256, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (branch2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " )\n",
+ " (repeat_2): Sequential(\n",
+ " (0): Block17(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(256, 896, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): Block17(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(256, 896, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (branch0): BasicConv2d(\n",
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+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (conv): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(256, 896, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (branch0): BasicConv2d(\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(256, 896, kernel_size=(1, 1), stride=(1, 1))\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
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+ " )\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
+ " (7): Block17(\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(256, 896, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (8): Block17(\n",
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+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
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+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
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+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(256, 896, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (9): Block17(\n",
+ " (branch0): BasicConv2d(\n",
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+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
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+ " (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(256, 896, kernel_size=(1, 1), stride=(1, 1))\n",
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+ " )\n",
+ " )\n",
+ " (mixed_7a): Mixed_7a(\n",
+ " (branch0): Sequential(\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (branch1): Sequential(\n",
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+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (bn): BatchNorm2d(256, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (branch3): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+ " )\n",
+ " (repeat_3): Sequential(\n",
+ " (0): Block8(\n",
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+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
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+ " (conv): Conv2d(1792, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (conv): Conv2d(192, 192, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(384, 1792, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): Block8(\n",
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+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(384, 1792, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(384, 1792, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (4): Block8(\n",
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+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (relu): ReLU()\n",
+ " )\n",
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+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(384, 1792, kernel_size=(1, 1), stride=(1, 1))\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (block8): Block8(\n",
+ " (branch0): BasicConv2d(\n",
+ " (conv): Conv2d(1792, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (branch1): Sequential(\n",
+ " (0): BasicConv2d(\n",
+ " (conv): Conv2d(1792, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (1): BasicConv2d(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " (2): BasicConv2d(\n",
+ " (conv): Conv2d(192, 192, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
+ " (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (relu): ReLU()\n",
+ " )\n",
+ " )\n",
+ " (conv2d): Conv2d(384, 1792, kernel_size=(1, 1), stride=(1, 1))\n",
+ " )\n",
+ " (avgpool_1a): AdaptiveAvgPool2d(output_size=1)\n",
+ " (dropout): Dropout(p=0.6, inplace=False)\n",
+ " (last_linear): Linear(in_features=1792, out_features=512, bias=False)\n",
+ " (last_bn): BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
+ " (logits): Linear(in_features=512, out_features=1, bias=True)\n",
+ ")"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model = InceptionResnetV1(\n",
+ " pretrained=\"vggface2\",\n",
+ " classify=True,\n",
+ " num_classes=1,\n",
+ " device=DEVICE\n",
+ ")\n",
+ "\n",
+ "checkpoint = torch.load(\"resnetinceptionv1_epoch_32.pth\", map_location=torch.device('cpu'))\n",
+ "model.load_state_dict(checkpoint['model_state_dict'])\n",
+ "model.to(DEVICE)\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a499194a",
+ "metadata": {},
+ "source": [
+ "# Model Inference "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "376e6cd6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict(input_image:Image.Image):\n",
+ " \"\"\"Predict the label of the input_image\"\"\"\n",
+ " face = mtcnn(input_image)\n",
+ " if face is None:\n",
+ " raise Exception('No face detected')\n",
+ " face = face.unsqueeze(0) # add the batch dimension\n",
+ " face = F.interpolate(face, size=(256, 256), mode='bilinear', align_corners=False)\n",
+ " \n",
+ " # convert the face into a numpy array to be able to plot it\n",
+ " prev_face = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()\n",
+ " prev_face = prev_face.astype('uint8')\n",
+ "\n",
+ " face = face.to(DEVICE)\n",
+ " face = face.to(torch.float32)\n",
+ " face = face / 255.0\n",
+ " face_image_to_plot = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()\n",
+ "\n",
+ " target_layers=[model.block8.branch1[-1]]\n",
+ " use_cuda = True if torch.cuda.is_available() else False\n",
+ " cam = GradCAM(model=model, target_layers=target_layers, use_cuda=use_cuda)\n",
+ " targets = [ClassifierOutputTarget(0)]\n",
+ "\n",
+ " grayscale_cam = cam(input_tensor=face, targets=targets, eigen_smooth=True)\n",
+ " grayscale_cam = grayscale_cam[0, :]\n",
+ " visualization = show_cam_on_image(face_image_to_plot, grayscale_cam, use_rgb=True)\n",
+ " face_with_mask = cv2.addWeighted(prev_face, 1, visualization, 0.5, 0)\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " output = torch.sigmoid(model(face).squeeze(0))\n",
+ " prediction = \"real\" if output.item() < 0.5 else \"fake\"\n",
+ " \n",
+ " real_prediction = 1 - output.item()\n",
+ " fake_prediction = output.item()\n",
+ " \n",
+ " confidences = {\n",
+ " 'real': real_prediction,\n",
+ " 'fake': fake_prediction\n",
+ " }\n",
+ " return confidences, face_with_mask\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14f47b5a",
+ "metadata": {},
+ "source": [
+ "# Gradio Interface"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d62177b5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running on local URL: http://127.0.0.1:7860\n",
+ "\n",
+ "To create a public link, set `share=True` in `launch()`.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "interface = gr.Interface(\n",
+ " fn=predict,\n",
+ " inputs=[\n",
+ " gr.inputs.Image(label=\"Input Image\", type=\"pil\")\n",
+ " ],\n",
+ " outputs=[\n",
+ " gr.outputs.Label(label=\"Class\"),\n",
+ " gr.outputs.Image(label=\"Face with Explainability\", type=\"pil\")\n",
+ " ],\n",
+ ").launch()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c0b293c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,3 +1,2 @@
----
-license: unknown
----
+# deepfake-detection
+Identify the images as real or fake using state-of-the-art AI models

kit_installer.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35b848850c1d74b16f6b5c85e9be74eaf6006d8c8a297013b91591f20363d8dd
+size 3690

requirements.txt

@@ -0,0 +1,7 @@
+jupyter==1.0.0
+gradio==3.23.0
+Pillow==9.4.0
+facenet-pytorch==2.5.2
+torch==1.11.0
+opencv-python==4.7.0.72
+grad-cam==1.4.6