first commit

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+*.pth
+data/
+.DS_Store

Dataset.py

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+import os
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+from random import shuffle
+import pandas as pd
+
+
+class PixWiseDataset():
+    def __init__(self, csvfile, map_size=14,
+                 smoothing=True, transform=None):
+        self.data = pd.read_csv(csvfile)
+        self.transform = transform
+        self.map_size = map_size
+        self.label_weight = 0.99 if smoothing else 1.0
+
+    def dataset(self):
+        images = []
+        labels = []
+        masks = []
+
+        for ind in self.data.index:
+            img_name = self.data.iloc[ind]['name']
+            img = Image.open(img_name)
+            # img = cv.resize(img, (224, 224))
+            # img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+            # img = np.moveaxis(img, 2, 0)
+            # img = np.asarray(img)
+
+            label = self.data.iloc[ind]['label']
+            if label == 0:
+                mask = np.ones((1, self.map_size, self.map_size), dtype=np.float32) * (1 - self.label_weight)
+            else:
+                mask = np.ones((1, self.map_size, self.map_size), dtype=np.float32) * (self.label_weight)
+
+            if self.transform:
+                img = self.transform(img)
+
+            images.append(img)
+            labels.append(label)
+            masks.append(mask)
+
+        labels = np.array(labels, dtype=np.float32)
+
+        dataset = [[images[i], masks[i], labels[i]] for i in range(len(images))]
+        return dataset

Loss.py

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+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class PixWiseBCELoss(nn.Module):
+    def __init__(self, beta=0.5):
+        super().__init__()
+        self.criterion = nn.BCELoss()
+        self.beta = beta
+
+    def forward(self, net_mask, net_label, target_mask, target_label):
+        pixel_loss = self.criterion(net_mask, target_mask)
+        binary_loss = self.criterion(net_label, target_label)
+        loss = pixel_loss * self.beta + binary_loss * (1 - self.beta)
+        return loss

Metrics.py

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+import torch
+import numpy
+
+
+def predict(mask, label, threshold=0.5, score_type='combined'):
+    with torch.no_grad():
+        if score_type == 'pixel':
+            score = torch.mean(mask, axis=(1, 2, 3))
+        elif score_type == 'binary':
+            score = label
+        else:
+            score = (torch.mean(mask, axis=(1, 2, 3)) + label) / 2
+
+        preds = (score > threshold).type(torch.FloatTensor)
+
+        return preds, score
+
+
+def test_accuracy(model, test_dl):
+    acc = 0
+    total = len(test_dl.dataset)
+    for img, mask, label in test_dl:
+        net_mask, net_label = model(img)
+        preds, _ = predict(net_mask, net_label)
+        ac = (preds == label).type(torch.FloatTensor)
+        acc += torch.sum(ac).item()
+    return (acc / total) * 100
+
+
+def test_loss(model, test_dl, loss_fn):
+    loss = 0
+    total = len(test_dl)
+    for img, mask, label in test_dl:
+        net_mask, net_label = model(img)
+        losses = loss_fn(net_mask, net_label, mask, label)
+        loss += torch.mean(losses).item()
+    return loss / total

Model.py

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+import torch
+from torch import nn
+from torchvision import models
+
+
+class DeePixBiS(nn.Module):
+    def __init__(self, pretrained=True):
+        super().__init__()
+        weights = pretrained if pretrained else None
+        dense = models.densenet161(weights=weights)
+        features = list(dense.features.children())
+        self.enc = nn.Sequential(*features[:8])
+        self.dec = nn.Conv2d(384, 1, kernel_size=1, stride=1, padding=0)
+        self.linear = nn.Linear(14 * 14, 1)
+
+    def forward(self, x):
+        enc = self.enc(x)
+        dec = self.dec(enc)
+        out_map = torch.sigmoid(dec)
+        # print(out_map.shape)
+        out = self.linear(out_map.view(-1, 14 * 14))
+        out = torch.sigmoid(out)
+        out = torch.flatten(out)
+        return out_map, out

README.md

@@ -1,12 +1,36 @@
----
-title: Fasd
-emoji: 🐢
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Face Anti-Spoofing using Deep-Pixel-wise-Binary-Supervision
+
+- Anti-Spoofing for Face Recognition task using the Deep Pixel-wise Binary Supervision Technique. The paper can be found here https://arxiv.org/pdf/1907.04047v1.pdf
+- This Project implements the DeePixBiS model using Python OpenCV, and the Pytorch Framework. This project is inspired from https://github.com/voqtuyen/deep-pix-bis-pad.pytorch
+- The Trained weights are already saved up as './DeePixBiS.pth' file which can be run on the model.
+- Training Data has been taken from the NUAA Imposter dataset (863 images subset)
+
+### Deep Pixel-wise Binary Supervision
+This framework uses CNN and densely connected neural network trained using both binary and pixel-wise binary supervision simultaneously.
+This is a frame level algorithm, which performs the task individually and independently on each frame, thus making computation and time feasable for practical use.
+Each pixel/patch of the frame is given a binary label depending on whether it is bonafide or an attack, trying to generate the depth-map of the image. Note that this framework does not generate a precise depth map, rather it does not need to. In the testing phase, the mean of this feature map is used as the score. If the score is greater than a threshold value, it is declared to be real.
+The model architecture uses the first 8 layers of the DenseNet-161 architecture, for feature extraction. 
+
+### About the Project
+
+We use the OpenCV library for the image preproccsing for the model. OpenCV offers several cascades for the task of object Detection. We use the Frontal-Face Haar Cascade to detect a "face" in the frame. Once a face is detected it has a bounded box to find its location, and the face is extracted, leaving aside the other non-important details in the frame. The training-data(frames) ready to pass through the model is trained using the Adam Optimizer. 
+The Loss function is a weighted sum using the binary and pixel-wise binary cross-entropy loss function.
+
+
+### Requirements
+
+- Python 3.6+
+- OpenCV
+- Numpy
+- PyTorch
+
+### Training the Model
+1. Run `python Train.py`
+2. After Training is complete the program will generate the file "./DeePixBiS.pth", containing weights of the model
+
+### Recognizing
+1. Run `python Test.py`
+
+### TODO
+1. Make directories for easy handling of python files.
+2. Add a config file for easy hyperparameters tuning.

Test.py

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+import cv2 as cv
+import torch
+import torch.nn as nn
+from torchvision import transforms
+import numpy as np
+from Model import DeePixBiS
+from Loss import PixWiseBCELoss
+from Metrics import predict, test_accuracy, test_loss
+
+model = DeePixBiS(pretrained=False)
+model.load_state_dict(torch.load('./DeePixBiS.pth'))
+model.eval()
+
+tfms = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+faceClassifier = cv.CascadeClassifier('Classifiers/haarface.xml')
+
+camera = cv.VideoCapture(0)
+
+while cv.waitKey(1) & 0xFF != ord('q'):
+    _, img = camera.read()
+    grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+    faces = faceClassifier.detectMultiScale(grey, scaleFactor=1.1, minNeighbors=4)
+
+    for x, y, w, h in faces:
+        faceRegion = img[y:y + h, x:x + w]
+        faceRegion = cv.cvtColor(faceRegion, cv.COLOR_BGR2RGB)
+        # cv.imshow('Test', faceRegion)
+
+        faceRegion = tfms(faceRegion)
+        faceRegion = faceRegion.unsqueeze(0)
+
+        mask, binary = model.forward(faceRegion)
+        res = torch.mean(mask).item()
+        # res = binary.item()
+
+        if res < 0.7:
+            label = f'Spoof {res:.2f}'
+            color = (0, 0, 255)
+        else:
+            label = f'Real {res:.2f}'
+            color = (0, 255, 0)
+        print(f'{label}: {res}')
+        cv.rectangle(img, (x, y), (x + w, y + h), color, 2)
+        cv.putText(img, label, (x, y + h + 30), cv.FONT_HERSHEY_COMPLEX, 1, color)
+
+    cv.imshow('Deep Pixel-wise Binary Supervision Anti-Spoofing', img)

Train.py

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+import torch
+import torch.nn as nn
+from torchvision.transforms import Compose, ToTensor, RandomHorizontalFlip, Normalize, Resize, RandomRotation
+import numpy as np
+from torch.utils.data import DataLoader
+from Dataset import PixWiseDataset
+from Model import DeePixBiS
+from Loss import PixWiseBCELoss
+from Metrics import predict, test_accuracy, test_loss
+from Trainer import Trainer
+
+model = DeePixBiS()
+model.load_state_dict(torch.load('./DeePixBiS.pth'))
+
+loss_fn = PixWiseBCELoss()
+
+opt = torch.optim.Adam(model.parameters(), lr=0.0001)
+
+train_tfms = Compose([Resize([224, 224]),
+                      RandomHorizontalFlip(),
+                      RandomRotation(10),
+                      ToTensor(),
+                      Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+
+test_tfms = Compose([Resize([224, 224]),
+                     ToTensor(),
+                     Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
+
+train_dataset = PixWiseDataset('./train_data.csv', transform=train_tfms)
+train_ds = train_dataset.dataset()
+
+val_dataset = PixWiseDataset('./test_data.csv', transform=test_tfms)
+val_ds = val_dataset.dataset()
+
+batch_size = 10
+train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=0, pin_memory=True)
+val_dl = DataLoader(val_ds, batch_size, shuffle=True, num_workers=0, pin_memory=True)
+
+# for x, y, z in val_dl:
+# 	_, zp = model(x)
+# 	print(zp)
+# 	print (z)
+# 	break
+
+# print(test_accuracy(model, train_dl))
+# print(test_loss(model, train_dl, loss_fn))
+
+# 5 epochs ran
+
+trainer = Trainer(train_dl, val_dl, model, 1, opt, loss_fn)
+
+print('Training Beginning\n')
+trainer.fit()
+print('\nTraining Complete')
+torch.save(model.state_dict(), './DeePixBiS.pth')

Trainer.py

@@ -0,0 +1,49 @@
+import torch
+import torch.nn as nn
+from Metrics import test_accuracy, test_loss
+
+
+class Trainer():
+    def __init__(self, train_dl, val_dl, model, epochs, opt, loss_fn, device='cpu'):
+        self.train_dl = train_dl
+        self.val_dl = val_dl
+        self.model = model.to(device)
+        self.epochs = epochs
+        self.opt = opt
+        self.loss_fn = loss_fn
+        self.device = device
+
+    def train_one_epoch(self, num):
+        print(f'\nEpoch ({num+1}/{self.epochs})')
+        print('----------------------------------')
+        # self.model.train()
+        for batch, (img, mask, label) in enumerate(self.train_dl):
+            img, mask, label = img.to(self.device), mask.to(self.device), label.to(self.device)
+            net_mask, net_label = self.model(img)
+            loss = self.loss_fn(net_mask, net_label, mask, label)
+
+            # Train
+            self.opt.zero_grad()
+            loss.backward()
+            self.opt.step()
+
+            if batch % 9 == 0:
+                print(f'Loss : {loss}')
+
+        # self.model.eval()
+        test_acc = test_accuracy(self.model, self.val_dl)
+        test_los = test_loss(self.model, self.val_dl, self.loss_fn)
+
+        print(f'Test Accuracy : {test_acc}  Test Loss : {test_los}')
+        return test_acc, test_los
+
+    def fit(self):
+        training_loss = []
+        training_acc = []
+        self.model.train()
+        for epoch in range(self.epochs):
+            train_acc, train_loss = self.train_one_epoch(epoch)
+            training_acc.append(train_acc)
+            training_loss.append(train_loss)
+
+        return training_acc, training_loss

app.py

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+import gradio as gr
+import cv2 as cv
+import torch
+from torchvision import transforms
+from Model import DeePixBiS
+
+
+labels = ['Live', 'Spoof']
+thresh = 0.45
+examples = [
+    'examples/1_1_21_2_33_scene_fake.jpg', 'examples/frame150_real.jpg',
+    'examples/1_2.avi_125_real.jpg', 'examples/1_3.avi_25_fake.jpg']
+device = torch.device("cpu")
+faceClassifier = cv.CascadeClassifier('Classifiers/haarface.xml')
+tfms = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+model = DeePixBiS(pretrained=False)
+model.load_state_dict(torch.load('./DeePixBiS.pth'))
+model.eval()
+
+
+def find_largest_face(faces):
+    largest_face = None
+    largest_area = 0
+
+    for (x, y, w, h) in faces:
+        area = w * h
+        if area > largest_area:
+            largest_area = area
+            largest_face = (x, y, w, h)
+    return largest_face
+
+
+def inference(img):
+    grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+    faces = faceClassifier.detectMultiScale(
+        grey, scaleFactor=1.1, minNeighbors=4)
+    face = find_largest_face(faces)
+    confidences = {}
+    if face is not None:
+        x, y, w, h = face
+        faceRegion = img[y:y + h, x:x + w]
+        faceRegion = cv.cvtColor(faceRegion, cv.COLOR_BGR2RGB)
+        faceRegion = tfms(faceRegion)
+        faceRegion = faceRegion.unsqueeze(0)
+        mask, binary = model.forward(faceRegion)
+        res = torch.mean(mask).item()
+        if res < thresh:
+            cls = 'Spoof'
+            color = (0, 0, 255)
+            res = 1 - res
+        else:
+            cls = 'Real'
+            color = (0, 255, 0)
+        label = f'{cls} {res:.2f}'
+        cv.rectangle(img, (x, y), (x + w, y + h), color, 2)
+        cv.putText(img, label, (x, y + h + 30),
+                   cv.FONT_HERSHEY_COMPLEX, 1, color)
+        confidences = {label: res}
+    return img, confidences
+
+
+if __name__ == '__main__':
+    demo = gr.Interface(
+        fn=inference,
+        inputs=[gr.Image(source='webcam', shape=None, type='numpy')],
+        outputs=["image", gr.Label(num_top_classes=2)],
+        examples=examples).queue(concurrency_count=2)
+    demo.launch(share=False)

data/Test

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+<<Test>>

main.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pytorch_vision",
+   "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": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+pytorch
+numpy
+opencv-python

test_data.csv

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+,name,label
+0,./data/images/fake_0001_00_01_03_93.jpg,0.0
+1,./data/images/genuine_0001_00_00_02_5.jpg,1.0
+2,./data/images/fake_0001_00_03_03_394.jpg,0.0
+3,./data/images/genuine_0001_00_00_01_16.jpg,1.0
+4,./data/images/fake_0001_00_04_03_83.jpg,0.0
+5,./data/images/fake_0001_00_04_02_170.jpg,0.0
+6,./data/images/genuine_0001_00_00_01_134.jpg,1.0
+7,./data/images/genuine_0001_00_00_01_347.jpg,1.0
+8,./data/images/genuine_0001_00_00_02_152.jpg,1.0
+9,./data/images/fake_0001_00_04_03_158.jpg,0.0
+10,./data/images/genuine_0001_00_00_01_228.jpg,1.0
+11,./data/images/fake_0001_00_08_03_444.jpg,0.0
+12,./data/images/fake_0001_00_00_01_143.jpg,0.0
+13,./data/images/fake_0001_00_04_03_23.jpg,0.0
+14,./data/images/genuine_0001_00_00_01_231.jpg,1.0
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+23,./data/images/fake_0001_00_01_02_388.jpg,0.0
+24,./data/images/fake_0001_00_03_02_189.jpg,0.0
+25,./data/images/genuine_0001_00_00_01_411.jpg,1.0
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+27,./data/images/fake_0001_00_02_03_245.jpg,0.0
+28,./data/images/fake_0001_00_04_03_349.jpg,0.0
+29,./data/images/genuine_0001_00_00_02_34.jpg,1.0
+30,./data/images/fake_0001_00_00_01_369.jpg,0.0
+31,./data/images/genuine_0001_00_00_02_283.jpg,1.0
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+33,./data/images/genuine_0001_00_00_02_434.jpg,1.0
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+35,./data/images/fake_0001_00_08_03_190.jpg,0.0
+36,./data/images/fake_0001_00_03_03_237.jpg,0.0
+37,./data/images/genuine_0001_00_00_02_231.jpg,1.0
+38,./data/images/fake_0001_00_03_03_21.jpg,0.0
+39,./data/images/fake_0001_00_02_02_242.jpg,0.0
+40,./data/images/genuine_0001_00_00_01_138.jpg,1.0
+41,./data/images/genuine_0001_00_00_02_358.jpg,1.0
+42,./data/images/genuine_0001_00_00_01_466.jpg,1.0
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+47,./data/images/genuine_0001_00_00_02_388.jpg,1.0
+48,./data/images/fake_0001_00_04_03_206.jpg,0.0
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+50,./data/images/genuine_0001_00_00_01_163.jpg,1.0
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+56,./data/images/fake_0001_00_02_02_314.jpg,0.0
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+60,./data/images/fake_0001_00_02_03_12.jpg,0.0
+61,./data/images/fake_0001_00_03_03_226.jpg,0.0
+62,./data/images/fake_0001_00_02_03_82.jpg,0.0

train_data.csv

@@ -0,0 +1,801 @@
+,name,label
+0,./data/images/fake_0001_00_02_03_207.jpg,0.0
+1,./data/images/fake_0001_00_01_03_461.jpg,0.0
+2,./data/images/genuine_0001_00_00_01_365.jpg,1.0
+3,./data/images/fake_0001_00_00_01_166.jpg,0.0
+4,./data/images/fake_0001_00_02_03_278.jpg,0.0
+5,./data/images/genuine_0001_00_00_02_86.jpg,1.0
+6,./data/images/fake_0001_00_01_03_428.jpg,0.0
+7,./data/images/fake_0001_00_08_03_303.jpg,0.0
+8,./data/images/fake_0001_00_01_03_38.jpg,0.0
+9,./data/images/genuine_0001_00_00_02_254.jpg,1.0
+10,./data/images/fake_0001_00_02_02_459.jpg,0.0
+11,./data/images/genuine_0001_00_00_02_258.jpg,1.0
+12,./data/images/fake_0001_00_00_01_382.jpg,0.0
+13,./data/images/genuine_0001_00_00_01_38.jpg,1.0
+14,./data/images/fake_0001_00_03_03_291.jpg,0.0
+15,./data/images/fake_0001_00_01_02_152.jpg,0.0
+16,./data/images/fake_0001_00_03_02_45.jpg,0.0
+17,./data/images/fake_0001_00_01_03_88.jpg,0.0
+18,./data/images/fake_0001_00_00_01_107.jpg,0.0
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