Alisher Amantay commited on
Commit
9067733
1 Parent(s): 9db68dc

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Browse files
.gitignore ADDED
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+ __pycache__/
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+ *.pth
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+ data/
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+ .DS_Store
Classifiers/haarface.xml ADDED
The diff for this file is too large to render. See raw diff
 
Dataset.py ADDED
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+ import os
2
+ import torch
3
+ import numpy as np
4
+ from PIL import Image
5
+ from torchvision import transforms
6
+ from random import shuffle
7
+ import pandas as pd
8
+
9
+
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+ class PixWiseDataset():
11
+ def __init__(self, csvfile, map_size=14,
12
+ smoothing=True, transform=None):
13
+ self.data = pd.read_csv(csvfile)
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+ self.transform = transform
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+ self.map_size = map_size
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+ self.label_weight = 0.99 if smoothing else 1.0
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+
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+ def dataset(self):
19
+ images = []
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+ labels = []
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+ masks = []
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+
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+ for ind in self.data.index:
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+ img_name = self.data.iloc[ind]['name']
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+ img = Image.open(img_name)
26
+ # img = cv.resize(img, (224, 224))
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+ # img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
28
+ # img = np.moveaxis(img, 2, 0)
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+ # img = np.asarray(img)
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+
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+ label = self.data.iloc[ind]['label']
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+ if label == 0:
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+ mask = np.ones((1, self.map_size, self.map_size), dtype=np.float32) * (1 - self.label_weight)
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+ else:
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+ mask = np.ones((1, self.map_size, self.map_size), dtype=np.float32) * (self.label_weight)
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+
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+ if self.transform:
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+ img = self.transform(img)
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+
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+ images.append(img)
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+ labels.append(label)
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+ masks.append(mask)
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+
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+ labels = np.array(labels, dtype=np.float32)
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+
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+ dataset = [[images[i], masks[i], labels[i]] for i in range(len(images))]
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+ return dataset
Loss.py ADDED
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+ import torch
2
+ from torch import nn
3
+ import torch.nn.functional as F
4
+
5
+
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+ class PixWiseBCELoss(nn.Module):
7
+ def __init__(self, beta=0.5):
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+ super().__init__()
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+ self.criterion = nn.BCELoss()
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+ self.beta = beta
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+
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+ def forward(self, net_mask, net_label, target_mask, target_label):
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+ pixel_loss = self.criterion(net_mask, target_mask)
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+ binary_loss = self.criterion(net_label, target_label)
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+ loss = pixel_loss * self.beta + binary_loss * (1 - self.beta)
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+ return loss
Metrics.py ADDED
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+ import torch
2
+ import numpy
3
+
4
+
5
+ def predict(mask, label, threshold=0.5, score_type='combined'):
6
+ with torch.no_grad():
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+ if score_type == 'pixel':
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+ score = torch.mean(mask, axis=(1, 2, 3))
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+ elif score_type == 'binary':
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+ score = label
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+ else:
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+ score = (torch.mean(mask, axis=(1, 2, 3)) + label) / 2
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+
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+ preds = (score > threshold).type(torch.FloatTensor)
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+
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+ return preds, score
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+
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+
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+ def test_accuracy(model, test_dl):
20
+ acc = 0
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+ total = len(test_dl.dataset)
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+ for img, mask, label in test_dl:
23
+ net_mask, net_label = model(img)
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+ preds, _ = predict(net_mask, net_label)
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+ ac = (preds == label).type(torch.FloatTensor)
26
+ acc += torch.sum(ac).item()
27
+ return (acc / total) * 100
28
+
29
+
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+ def test_loss(model, test_dl, loss_fn):
31
+ loss = 0
32
+ total = len(test_dl)
33
+ for img, mask, label in test_dl:
34
+ net_mask, net_label = model(img)
35
+ losses = loss_fn(net_mask, net_label, mask, label)
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+ loss += torch.mean(losses).item()
37
+ return loss / total
Model.py ADDED
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1
+ import torch
2
+ from torch import nn
3
+ from torchvision import models
4
+
5
+
6
+ class DeePixBiS(nn.Module):
7
+ def __init__(self, pretrained=True):
8
+ super().__init__()
9
+ weights = pretrained if pretrained else None
10
+ dense = models.densenet161(weights=weights)
11
+ features = list(dense.features.children())
12
+ self.enc = nn.Sequential(*features[:8])
13
+ self.dec = nn.Conv2d(384, 1, kernel_size=1, stride=1, padding=0)
14
+ self.linear = nn.Linear(14 * 14, 1)
15
+
16
+ def forward(self, x):
17
+ enc = self.enc(x)
18
+ dec = self.dec(enc)
19
+ out_map = torch.sigmoid(dec)
20
+ # print(out_map.shape)
21
+ out = self.linear(out_map.view(-1, 14 * 14))
22
+ out = torch.sigmoid(out)
23
+ out = torch.flatten(out)
24
+ return out_map, out
README.md CHANGED
@@ -1,12 +1,36 @@
1
- ---
2
- title: Fasd
3
- emoji: 🐢
4
- colorFrom: pink
5
- colorTo: purple
6
- sdk: gradio
7
- sdk_version: 3.21.0
8
- app_file: app.py
9
- pinned: false
10
- ---
11
-
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Face Anti-Spoofing using Deep-Pixel-wise-Binary-Supervision
2
+
3
+ - 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
4
+ - 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
5
+ - The Trained weights are already saved up as './DeePixBiS.pth' file which can be run on the model.
6
+ - Training Data has been taken from the NUAA Imposter dataset (863 images subset)
7
+
8
+ ### Deep Pixel-wise Binary Supervision
9
+ This framework uses CNN and densely connected neural network trained using both binary and pixel-wise binary supervision simultaneously.
10
+ 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.
11
+ 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.
12
+ The model architecture uses the first 8 layers of the DenseNet-161 architecture, for feature extraction.
13
+
14
+ ### About the Project
15
+
16
+ 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.
17
+ The Loss function is a weighted sum using the binary and pixel-wise binary cross-entropy loss function.
18
+
19
+
20
+ ### Requirements
21
+
22
+ - Python 3.6+
23
+ - OpenCV
24
+ - Numpy
25
+ - PyTorch
26
+
27
+ ### Training the Model
28
+ 1. Run `python Train.py`
29
+ 2. After Training is complete the program will generate the file "./DeePixBiS.pth", containing weights of the model
30
+
31
+ ### Recognizing
32
+ 1. Run `python Test.py`
33
+
34
+ ### TODO
35
+ 1. Make directories for easy handling of python files.
36
+ 2. Add a config file for easy hyperparameters tuning.
Test.py ADDED
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1
+ import cv2 as cv
2
+ import torch
3
+ import torch.nn as nn
4
+ from torchvision import transforms
5
+ import numpy as np
6
+ from Model import DeePixBiS
7
+ from Loss import PixWiseBCELoss
8
+ from Metrics import predict, test_accuracy, test_loss
9
+
10
+ model = DeePixBiS(pretrained=False)
11
+ model.load_state_dict(torch.load('./DeePixBiS.pth'))
12
+ model.eval()
13
+
14
+ tfms = transforms.Compose([
15
+ transforms.ToPILImage(),
16
+ transforms.Resize((224, 224)),
17
+ transforms.ToTensor(),
18
+ transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
19
+ ])
20
+
21
+ faceClassifier = cv.CascadeClassifier('Classifiers/haarface.xml')
22
+
23
+ camera = cv.VideoCapture(0)
24
+
25
+ while cv.waitKey(1) & 0xFF != ord('q'):
26
+ _, img = camera.read()
27
+ grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
28
+ faces = faceClassifier.detectMultiScale(grey, scaleFactor=1.1, minNeighbors=4)
29
+
30
+ for x, y, w, h in faces:
31
+ faceRegion = img[y:y + h, x:x + w]
32
+ faceRegion = cv.cvtColor(faceRegion, cv.COLOR_BGR2RGB)
33
+ # cv.imshow('Test', faceRegion)
34
+
35
+ faceRegion = tfms(faceRegion)
36
+ faceRegion = faceRegion.unsqueeze(0)
37
+
38
+ mask, binary = model.forward(faceRegion)
39
+ res = torch.mean(mask).item()
40
+ # res = binary.item()
41
+
42
+ if res < 0.7:
43
+ label = f'Spoof {res:.2f}'
44
+ color = (0, 0, 255)
45
+ else:
46
+ label = f'Real {res:.2f}'
47
+ color = (0, 255, 0)
48
+ print(f'{label}: {res}')
49
+ cv.rectangle(img, (x, y), (x + w, y + h), color, 2)
50
+ cv.putText(img, label, (x, y + h + 30), cv.FONT_HERSHEY_COMPLEX, 1, color)
51
+
52
+ cv.imshow('Deep Pixel-wise Binary Supervision Anti-Spoofing', img)
Train.py ADDED
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1
+ import torch
2
+ import torch.nn as nn
3
+ from torchvision.transforms import Compose, ToTensor, RandomHorizontalFlip, Normalize, Resize, RandomRotation
4
+ import numpy as np
5
+ from torch.utils.data import DataLoader
6
+ from Dataset import PixWiseDataset
7
+ from Model import DeePixBiS
8
+ from Loss import PixWiseBCELoss
9
+ from Metrics import predict, test_accuracy, test_loss
10
+ from Trainer import Trainer
11
+
12
+ model = DeePixBiS()
13
+ model.load_state_dict(torch.load('./DeePixBiS.pth'))
14
+
15
+ loss_fn = PixWiseBCELoss()
16
+
17
+ opt = torch.optim.Adam(model.parameters(), lr=0.0001)
18
+
19
+ train_tfms = Compose([Resize([224, 224]),
20
+ RandomHorizontalFlip(),
21
+ RandomRotation(10),
22
+ ToTensor(),
23
+ Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
24
+
25
+ test_tfms = Compose([Resize([224, 224]),
26
+ ToTensor(),
27
+ Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
28
+
29
+ train_dataset = PixWiseDataset('./train_data.csv', transform=train_tfms)
30
+ train_ds = train_dataset.dataset()
31
+
32
+ val_dataset = PixWiseDataset('./test_data.csv', transform=test_tfms)
33
+ val_ds = val_dataset.dataset()
34
+
35
+ batch_size = 10
36
+ train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=0, pin_memory=True)
37
+ val_dl = DataLoader(val_ds, batch_size, shuffle=True, num_workers=0, pin_memory=True)
38
+
39
+ # for x, y, z in val_dl:
40
+ # _, zp = model(x)
41
+ # print(zp)
42
+ # print (z)
43
+ # break
44
+
45
+ # print(test_accuracy(model, train_dl))
46
+ # print(test_loss(model, train_dl, loss_fn))
47
+
48
+ # 5 epochs ran
49
+
50
+ trainer = Trainer(train_dl, val_dl, model, 1, opt, loss_fn)
51
+
52
+ print('Training Beginning\n')
53
+ trainer.fit()
54
+ print('\nTraining Complete')
55
+ torch.save(model.state_dict(), './DeePixBiS.pth')
Trainer.py ADDED
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1
+ import torch
2
+ import torch.nn as nn
3
+ from Metrics import test_accuracy, test_loss
4
+
5
+
6
+ class Trainer():
7
+ def __init__(self, train_dl, val_dl, model, epochs, opt, loss_fn, device='cpu'):
8
+ self.train_dl = train_dl
9
+ self.val_dl = val_dl
10
+ self.model = model.to(device)
11
+ self.epochs = epochs
12
+ self.opt = opt
13
+ self.loss_fn = loss_fn
14
+ self.device = device
15
+
16
+ def train_one_epoch(self, num):
17
+ print(f'\nEpoch ({num+1}/{self.epochs})')
18
+ print('----------------------------------')
19
+ # self.model.train()
20
+ for batch, (img, mask, label) in enumerate(self.train_dl):
21
+ img, mask, label = img.to(self.device), mask.to(self.device), label.to(self.device)
22
+ net_mask, net_label = self.model(img)
23
+ loss = self.loss_fn(net_mask, net_label, mask, label)
24
+
25
+ # Train
26
+ self.opt.zero_grad()
27
+ loss.backward()
28
+ self.opt.step()
29
+
30
+ if batch % 9 == 0:
31
+ print(f'Loss : {loss}')
32
+
33
+ # self.model.eval()
34
+ test_acc = test_accuracy(self.model, self.val_dl)
35
+ test_los = test_loss(self.model, self.val_dl, self.loss_fn)
36
+
37
+ print(f'Test Accuracy : {test_acc} Test Loss : {test_los}')
38
+ return test_acc, test_los
39
+
40
+ def fit(self):
41
+ training_loss = []
42
+ training_acc = []
43
+ self.model.train()
44
+ for epoch in range(self.epochs):
45
+ train_acc, train_loss = self.train_one_epoch(epoch)
46
+ training_acc.append(train_acc)
47
+ training_loss.append(train_loss)
48
+
49
+ return training_acc, training_loss
app.py ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ import cv2 as cv
3
+ import torch
4
+ from torchvision import transforms
5
+ from Model import DeePixBiS
6
+
7
+
8
+ labels = ['Live', 'Spoof']
9
+ thresh = 0.45
10
+ examples = [
11
+ 'examples/1_1_21_2_33_scene_fake.jpg', 'examples/frame150_real.jpg',
12
+ 'examples/1_2.avi_125_real.jpg', 'examples/1_3.avi_25_fake.jpg']
13
+ device = torch.device("cpu")
14
+ faceClassifier = cv.CascadeClassifier('Classifiers/haarface.xml')
15
+ tfms = transforms.Compose([
16
+ transforms.ToPILImage(),
17
+ transforms.Resize((224, 224)),
18
+ transforms.ToTensor(),
19
+ transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
20
+ ])
21
+ model = DeePixBiS(pretrained=False)
22
+ model.load_state_dict(torch.load('./DeePixBiS.pth'))
23
+ model.eval()
24
+
25
+
26
+ def find_largest_face(faces):
27
+ largest_face = None
28
+ largest_area = 0
29
+
30
+ for (x, y, w, h) in faces:
31
+ area = w * h
32
+ if area > largest_area:
33
+ largest_area = area
34
+ largest_face = (x, y, w, h)
35
+ return largest_face
36
+
37
+
38
+ def inference(img):
39
+ grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
40
+ faces = faceClassifier.detectMultiScale(
41
+ grey, scaleFactor=1.1, minNeighbors=4)
42
+ face = find_largest_face(faces)
43
+ confidences = {}
44
+ if face is not None:
45
+ x, y, w, h = face
46
+ faceRegion = img[y:y + h, x:x + w]
47
+ faceRegion = cv.cvtColor(faceRegion, cv.COLOR_BGR2RGB)
48
+ faceRegion = tfms(faceRegion)
49
+ faceRegion = faceRegion.unsqueeze(0)
50
+ mask, binary = model.forward(faceRegion)
51
+ res = torch.mean(mask).item()
52
+ if res < thresh:
53
+ cls = 'Spoof'
54
+ color = (0, 0, 255)
55
+ res = 1 - res
56
+ else:
57
+ cls = 'Real'
58
+ color = (0, 255, 0)
59
+ label = f'{cls} {res:.2f}'
60
+ cv.rectangle(img, (x, y), (x + w, y + h), color, 2)
61
+ cv.putText(img, label, (x, y + h + 30),
62
+ cv.FONT_HERSHEY_COMPLEX, 1, color)
63
+ confidences = {label: res}
64
+ return img, confidences
65
+
66
+
67
+ if __name__ == '__main__':
68
+ demo = gr.Interface(
69
+ fn=inference,
70
+ inputs=[gr.Image(source='webcam', shape=None, type='numpy')],
71
+ outputs=["image", gr.Label(num_top_classes=2)],
72
+ examples=examples).queue(concurrency_count=2)
73
+ demo.launch(share=False)
data/Test ADDED
@@ -0,0 +1 @@
 
 
1
+ <<Test>>
examples/1_1_21_2_33_scene_fake.jpg ADDED
examples/1_2.avi_125_real.jpg ADDED
examples/1_3.avi_25_fake.jpg ADDED
examples/frame150_real.jpg ADDED
main.ipynb ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 1,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "import cv2"
10
+ ]
11
+ },
12
+ {
13
+ "cell_type": "code",
14
+ "execution_count": null,
15
+ "metadata": {},
16
+ "outputs": [],
17
+ "source": []
18
+ }
19
+ ],
20
+ "metadata": {
21
+ "kernelspec": {
22
+ "display_name": "pytorch_vision",
23
+ "language": "python",
24
+ "name": "python3"
25
+ },
26
+ "language_info": {
27
+ "codemirror_mode": {
28
+ "name": "ipython",
29
+ "version": 3
30
+ },
31
+ "file_extension": ".py",
32
+ "mimetype": "text/x-python",
33
+ "name": "python",
34
+ "nbconvert_exporter": "python",
35
+ "pygments_lexer": "ipython3",
36
+ "version": "3.10.9"
37
+ },
38
+ "orig_nbformat": 4
39
+ },
40
+ "nbformat": 4,
41
+ "nbformat_minor": 2
42
+ }
requirements.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ pytorch
2
+ numpy
3
+ opencv-python
test_data.csv ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ,name,label
2
+ 0,./data/images/fake_0001_00_01_03_93.jpg,0.0
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+ 1,./data/images/genuine_0001_00_00_02_5.jpg,1.0
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+ 2,./data/images/fake_0001_00_03_03_394.jpg,0.0
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+ 3,./data/images/genuine_0001_00_00_01_16.jpg,1.0
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+ 4,./data/images/fake_0001_00_04_03_83.jpg,0.0
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+ 5,./data/images/fake_0001_00_04_02_170.jpg,0.0
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+ 6,./data/images/genuine_0001_00_00_01_134.jpg,1.0
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+ 7,./data/images/genuine_0001_00_00_01_347.jpg,1.0
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+ 8,./data/images/genuine_0001_00_00_02_152.jpg,1.0
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+ 9,./data/images/fake_0001_00_04_03_158.jpg,0.0
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+ 10,./data/images/genuine_0001_00_00_01_228.jpg,1.0
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+ 11,./data/images/fake_0001_00_08_03_444.jpg,0.0
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+ 14,./data/images/genuine_0001_00_00_01_231.jpg,1.0
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+ 15,./data/images/fake_0001_00_02_03_429.jpg,0.0
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+ 16,./data/images/fake_0001_00_00_01_152.jpg,0.0
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