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deepfake_detection.py
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from torchvision import datasets, models, transforms
import os
import torch
from torch.autograd import Variable
from skimage import io
from scipy import fftpack
import numpy as np
from torch import nn
import datetime
from models import encoder_deepfake
from models import fen
import torch.nn.functional as F
from sklearn.metrics import accuracy_score
from sklearn import metrics
import argparse
#################################################################################################################
# HYPER PARAMETERS INITIALIZING
parser = argparse.ArgumentParser()
parser.add_argument('--lr', default=0.0001, type=float, help='learning rate')
parser.add_argument('--data_train',default='/mnt/scratch/asnanivi/GAN_data_6/set_1/train',help='root directory for training data')
parser.add_argument('--data_test',default='/mnt/scratch/asnanivi/GAN_data_6/set_1/test',help='root directory for testing data')
parser.add_argument('--ground_truth_dir',default='./',help='directory for ground truth')
parser.add_argument('--seed', default=1, type=int, help='manual seed')
parser.add_argument('--batch_size', default=16, type=int, help='batch size')
parser.add_argument('--savedir', default='/mnt/scratch/asnanivi/runs')
parser.add_argument('--model_dir', default='./models')
opt = parser.parse_args()
print(opt)
print("Random Seed: ", opt.seed)
device=torch.device("cuda:0")
torch.backends.deterministic = True
torch.manual_seed(opt.seed)
torch.cuda.manual_seed_all(opt.seed)
sig = str(datetime.datetime.now())
train_path=opt.data_train
test_path=opt.data_test
save_dir=opt.savedir
os.makedirs('%s/logs/%s' % (save_dir, sig), exist_ok=True)
os.makedirs('%s/result_2/%s' % (save_dir, sig), exist_ok=True)
transform_train = transforms.Compose([
transforms.Resize((128,128)),
transforms.ToTensor(),
transforms.Normalize((0.6490, 0.6490, 0.6490), (0.1269, 0.1269, 0.1269))
])
train_set=datasets.ImageFolder(train_path, transform_train)
test_set=datasets.ImageFolder(test_path, transform_train)
train_loader = torch.utils.data.DataLoader(train_set,batch_size=32,shuffle =True, num_workers=1)
test_loader = torch.utils.data.DataLoader(test_set,batch_size=32,shuffle =True, num_workers=1)
model=fen.DnCNN(num_layers=31).to(device)
model_params = list(model.parameters())
optimizer = torch.optim.Adam(model_params, lr=opt.lr)
l1=torch.nn.MSELoss().to(device)
l_c = torch.nn.CrossEntropyLoss().to(device)
model_2=encoder_deepfake.encoder(num_hidden=512).to(device)
optimizer_2 = torch.optim.Adam(model_2.parameters(), lr=opt.lr)
state = {
'state_dict_cnn':model.state_dict(),
'optimizer_1': optimizer.state_dict(),
'state_dict_class':model_2.state_dict(),
'optimizer_2': optimizer_2.state_dict()
}
state1 = torch.load(opt.model_dir)
optimizer.load_state_dict(state1['optimizer_1'])
model.load_state_dict(state1['state_dict_cnn'])
optimizer_2.load_state_dict(state1['optimizer_2'])
model_2.load_state_dict(state1['state_dict_class'])
def train(batch, labels):
model.train()
model_2.train()
y,low_freq_part,max_value ,y_orig,residual, y_trans,residual_gray =model(batch.type(torch.cuda.FloatTensor))
classes, features=model_2(residual)
classes_f=torch.max(classes, dim=1)[0]
n=25
zero=torch.zeros([y.shape[0],2*n+1,2*n+1], dtype=torch.float32).to(device)
zero_1=torch.zeros(residual_gray.shape, dtype=torch.float32).to(device)
loss1=0.05*l1(low_freq_part,zero).to(device)
loss2=-0.001*max_value.to(device)
loss3 = 0.01*l1(residual_gray,zero_1).to(device)
loss_c =20*l_c(classes,labels.type(torch.cuda.LongTensor))
loss5=0.1*l1(y,y_trans).to(device)
loss=(loss1+loss2+loss3+loss_c+loss5)
optimizer.zero_grad()
optimizer_2.zero_grad()
loss.backward()
optimizer.step()
optimizer_2.step()
return y, loss.item(), loss1.item(),loss2.item(),loss3.item(),loss_c.item(),loss5.item(),y_orig, features,residual,torch.max(classes, dim=1)[1]
def test(batch, labels):
model.eval()
model_2.eval()
with torch.no_grad():
y,low_freq_part,max_value ,y_orig,residual, y_trans,residual_gray =model(batch.type(torch.cuda.FloatTensor))
y_2=torch.unsqueeze(y.clone(),1)
classes, features=model_2(residual)
classes_f=torch.max(classes, dim=1)[0]
n=25
zero=torch.zeros([y.shape[0],2*n+1,2*n+1], dtype=torch.float32).to(device)
zero_1=torch.zeros(residual_gray.shape, dtype=torch.float32).to(device)
loss1=0.5*l1(low_freq_part,zero).to(device)
loss2=-0.001*max_value.to(device)
loss3 = 0.01*l1(residual_gray,zero_1).to(device)
loss_c =20*l_c(classes,labels.type(torch.cuda.LongTensor))
loss5=0.1*l1(y,y_trans).to(device)
loss=(loss1+loss2+loss3+loss_c+loss5)
return y, loss.item(), loss1.item(),loss2.item(),loss3.item(),loss_c.item(),loss5.item(),y_orig, features,residual,torch.max(classes, dim=1)[1], classes[:,1]
print(len(train_set))
print(len(test_set))
print(train_set.class_to_idx)
print(test_set.class_to_idx)
epochs=10
for epoch in range(epochs):
all_y=[]
all_y_test=[]
flag1=0
count=0
itr=0
for batch_idx, (inputs,labels) in enumerate(train_loader):
out,loss,loss1,loss2,loss3,loss4,loss5, out_orig,features,residual,pred=train(Variable(torch.FloatTensor(inputs)),Variable(torch.LongTensor(labels)))
all_y.append(np.asarray(labels))
itr+=32
if( float(itr%32000)==0.0):
for batch_idx_test, (inputs_test,labels_test) in enumerate(test_loader):
out,loss,loss1,loss2,loss3,loss4,loss5, out_orig,features,residual,pred_1,scores=test(Variable(torch.FloatTensor(inputs_test)),Variable(torch.LongTensor(labels_test)))
if flag1==0:
all_y_test=labels_test
all_y_pred_test=pred_1.detach()
all_scores=scores.detach()
flag1=1
else:
all_y_pred_test=torch.cat([all_y_pred_test,pred_1.detach()], dim=0)
all_y_test=torch.cat([all_y_test,labels_test], dim=0)
all_scores=torch.cat([all_scores,scores], dim=0)
fpr1, tpr1, thresholds1 = metrics.roc_curve(all_y_test, np.asarray(all_scores.cpu()), pos_label=1)
print("testing AUC is:", metrics.auc(fpr1, tpr1))
count+=1
print('epoch=',epoch)