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.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.pt
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+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/
+cover/
+
+# 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
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .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
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__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/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea

app.py

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+import streamlit as st
+import torch
+import torchvision.transforms as transforms
+# from torch import nn
+# import cv2
+# import numpy as np
+# import torch
+# import torchvision
+# from torchvision import models, transforms
+# from torch.utils.data import DataLoader
+# from torch.utils.data.dataset import Dataset
+# import os
+# import matplotlib.pyplot as plt
+# import face_recognition
+# import sys
+# import time
+# from torch.autograd import Variable
+from function import Model, validation_dataset, predict
+
+# Set Streamlit page config
+st.set_page_config(
+                    page_title="DeepFake Detection", 
+                    layout="centered",
+                    page_icon=":mask:"
+                )
+
+# Load the pre-trained model
+@st.cache_resource
+def load_model():
+    model = Model(num_classes=2).cuda()
+    model_path = "model.pt"  # Update with actual model path
+    model.load_state_dict(torch.load(model_path))
+    model.eval()
+    return model
+
+model = load_model()
+
+st.title("DeepFake Detection App")
+st.divider()
+st.sidebar.header("Upload Video")
+uploaded_video = st.sidebar.file_uploader("Choose a video file", type=["mp4", "avi", "mov"])
+
+if uploaded_video:
+    st.sidebar.write("Video uploaded successfully!")
+
+    # Save the uploaded video locally for processing
+    with open("uploaded_video.mp4", "wb") as f:
+        f.write(uploaded_video.getbuffer())
+
+    st.video("uploaded_video.mp4")
+
+    # Preprocess and analyze the video
+    with st.spinner("Processing video..."):
+
+        try:
+            # Validation dataset
+            video_dataset = validation_dataset(
+                video_names=["uploaded_video.mp4"], 
+                sequence_length=20, 
+                transform=transforms.Compose([
+                    transforms.ToPILImage(),
+                    transforms.Resize((112, 112)),
+                    transforms.ToTensor(),
+                    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+                ])
+            )
+
+            video_frames = video_dataset[0]  # Extract frames from video
+            if video_frames is not None:
+                prediction = predict(model, video_frames)
+                st.subheader("Prediction: ")
+                st.write(f"{'REAL' if prediction[0] == 1 else 'FAKE'}")
+
+                st.subheader("Confidence: ")
+                st.write(f"{prediction[1]:.2f}%")
+            else:
+                st.error("No faces detected in the video.")
+
+        except Exception as e:
+            st.error(f"An error occurred during processing: {e}")
+
+else:
+    st.sidebar.write("Upload a video file.")

function.py

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+import torch
+import torchvision
+from torchvision import models, transforms
+from torch.utils.data import DataLoader
+from torch.utils.data.dataset import Dataset
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+import face_recognition
+import sys
+import time
+from torch.autograd import Variable
+from torch import nn
+
+class Model(nn.Module):
+  def __init__(self, num_classes, latent_dim=2048, lstm_layers=1, hidden_dim=2048, bidirectional=False):
+    super(Model, self).__init__()
+    model = models.resnext50_32x4d(pretrained=True)
+    self.model = nn.Sequential(*list(model.children())[:-2])
+    self.lstm = nn.LSTM(latent_dim, hidden_dim, lstm_layers, bidirectional)
+    self.relu = nn.LeakyReLU()
+    self.dp = nn.Dropout(0.4)
+    self.linear1 = nn.Linear(2048, num_classes)
+    self.avgpool = nn.AdaptiveAvgPool2d(1)
+
+  def forward(self, x):
+    batch_size, seq_length, c, h, w = x.shape
+    x = x.view(batch_size * seq_length, c, h, w)
+    fmap = self.model(x)
+    x = self.avgpool(fmap)
+    x = x.view(batch_size, seq_length, 2048)
+    x_lstm, _ = self.lstm(x, None)
+    return fmap, self.dp(self.linear1(x_lstm[:, -1, :]))
+  
+im_size = 112
+mean = [0.485, 0.456, 0.406]
+std = [0.229, 0.224, 0.225]
+sm = nn.Softmax()
+inv_normalize = transforms.Normalize(mean=-1*np.divide(mean, std), std=np.divide([1,1,1], std))
+
+def im_convert(tensor):
+  image = tensor.to('cpu').clone().detach()
+  image = image.squeeze()
+  image = inv_normalize(image)
+  image = image.numpy()
+  image = image.transpose(1, 2, 0)
+  image = image.clip(0, 1)
+  cv2.imwrite('./2.png', image*255)
+  return image
+
+def predict(model, img, path = './'):
+  fmap, logits = model(img.to('cuda'))
+  params = list(model.parameters())
+  weight_softmax = model.linear1.weight.detach().cpu().numpy()
+  logits = sm(logits)
+  _, prediction = torch.max(logits,1)
+  confidence = logits[:, int(prediction.item())].item()*100
+  print('confidence of prediction', logits[:, int(prediction.item())].item()*100)
+  idx = np.argmax(logits.detach().cpu().numpy())
+  bz, nc, h, w = fmap.shape
+  out = np.dot(fmap[-1].detach().cpu().numpy().reshape((nc, h*w)).T, weight_softmax[idx, :].T)
+  predict = out.reshape(h, w)
+  predict = predict - np.min(predict)
+  predict_img = predict / np.max(predict)
+  predict_img = np.uint8(255*predict_img)
+  out = cv2.resize(predict_img, (im_size, im_size))
+  heatmap = cv2.applyColorMap(out, cv2.COLORMAP_JET)
+  img = im_convert(img[:, -1, :, :, :])
+  result = heatmap * 0.5 + img*0.8*255
+  cv2.imwrite('./1.png', result)
+  result1 = heatmap * 0.5/255 + img*0.8
+  r, g, b = cv2.split(result1)
+  result1 = cv2.merge((r, g, b))
+  plt.imshow(result1)
+  plt.show()
+  return [int(prediction.item()), confidence]
+
+class validation_dataset(Dataset):
+  def __init__(self, video_names, sequence_length = 60, transform = None):
+    self.video_names = video_names
+    self.transform = transform
+    self.count = sequence_length
+  def __len__(self):
+    return len(self.video_names)
+  def __getitem__(self, idx):
+    video_path = self.video_names[idx]
+    frames = []
+    a = int(100/self.count)
+    first_frame = np.random.randint(0, a)
+    for i, frame in enumerate(self.frame_extract(video_path)):
+      faces = face_recognition.face_locations(frame)
+      try:
+        top, right, bottom, left = faces[0]
+        frame = frame[top:bottom, left:right, :]
+      except:
+        pass
+      if self.transform:
+        frames.append(self.transform(frame))
+      if(len(frames) == self.count):
+        break
+    if len(frames)==0:
+      print('no face found in the video')
+      return None
+    frames = torch.stack(frames)
+    frames = frames[:self.count]
+    return frames.unsqueeze(0)
+
+  def frame_extract(self, path):
+    vidObj = cv2.VideoCapture(path)
+    success = 1
+    while success:
+      success, image = vidObj.read()
+      if success:
+        yield image
+
+  def im_plot(tensor):
+    image = tensor.cpu().numpy().transpose(1,2,0)
+    b,g,r = cv2.split(image)
+    image = cv2.merge((r,g,b))
+    image = image*[0.22803, 0.22145, 0.216989] + [0.43216, 0.394666, 0.37645]
+    image = image*255.0
+    plt.imshow(image.astype(int))
+    plt.show()

model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd1697b63ea963756fc603c33bbc27772d07548ce51e395b01b1bbdf7bcfe3f2
+size 226547517

packages.txt

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+cmake

requirements.txt

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+torch==2.5.1
+torchvision==0.20.1
+opencv-python
+face_recognition
+streamlit
+matplotlib
+numpy
+