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"cells": [
{
"cell_type": "markdown",
"id": "1e0e7b4a",
"metadata": {},
"source": [
"# DeepShield: FaceForensics++ ViT Training \n",
"Run this entirely in Google Colab.\n",
"**Before running**:\n",
"1. Go to `Runtime` -> `Change runtime type` -> select **T4 GPU**.\n",
"2. Run the cells below sequentially.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4fe293e7",
"metadata": {},
"outputs": [],
"source": [
"!pip install timm transformers datasets accelerate evaluate opencv-python\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9387c0f",
"metadata": {},
"outputs": [],
"source": [
"# We create the download script inside the Colab environment\n",
"download_script = '''#!/usr/bin/env python\n",
"import argparse\n",
"import os\n",
"import urllib.request\n",
"import tempfile\n",
"import time\n",
"import sys\n",
"import json\n",
"from tqdm import tqdm\n",
"from os.path import join\n",
"\n",
"FILELIST_URL = 'misc/filelist.json'\n",
"DEEPFEAKES_DETECTION_URL = 'misc/deepfake_detection_filenames.json'\n",
"DEEPFAKES_MODEL_NAMES = ['decoder_A.h5', 'decoder_B.h5', 'encoder.h5',]\n",
"DATASETS = {\n",
" 'original': 'original_sequences/youtube',\n",
" 'Deepfakes': 'manipulated_sequences/Deepfakes',\n",
" 'Face2Face': 'manipulated_sequences/Face2Face',\n",
" 'FaceShifter': 'manipulated_sequences/FaceShifter',\n",
" 'FaceSwap': 'manipulated_sequences/FaceSwap',\n",
" 'NeuralTextures': 'manipulated_sequences/NeuralTextures'\n",
"}\n",
"ALL_DATASETS = ['original', 'Deepfakes', 'Face2Face', 'FaceShifter', 'FaceSwap', 'NeuralTextures']\n",
"COMPRESSION = ['raw', 'c23', 'c40']\n",
"TYPE = ['videos']\n",
"\n",
"def download_file(url, out_file):\n",
" os.makedirs(os.path.dirname(out_file), exist_ok=True)\n",
" if not os.path.isfile(out_file):\n",
" urllib.request.urlretrieve(url, out_file)\n",
"\n",
"def main():\n",
" parser = argparse.ArgumentParser()\n",
" parser.add_argument('output_path', type=str)\n",
" parser.add_argument('-d', '--dataset', type=str, default='all')\n",
" parser.add_argument('-c', '--compression', type=str, default='c40')\n",
" parser.add_argument('-t', '--type', type=str, default='videos')\n",
" parser.add_argument('-n', '--num_videos', type=int, default=50) # Small amount for tutorial\n",
" args = parser.parse_args()\n",
" \n",
" base_url = 'http://kaldir.vc.in.tum.de/faceforensics/v3/'\n",
" \n",
" datasets = [args.dataset] if args.dataset != 'all' else ALL_DATASETS\n",
" for dataset in datasets:\n",
" dataset_path = DATASETS[dataset]\n",
" print(f'Downloading {args.compression} of {dataset}')\n",
" \n",
" file_pairs = json.loads(urllib.request.urlopen(base_url + FILELIST_URL).read().decode(\"utf-8\"))\n",
" filelist = []\n",
" if 'original' in dataset_path:\n",
" for pair in file_pairs:\n",
" filelist += pair\n",
" else:\n",
" for pair in file_pairs:\n",
" filelist.append('_'.join(pair))\n",
" filelist.append('_'.join(pair[::-1]))\n",
" \n",
" filelist = filelist[:args.num_videos]\n",
" dataset_videos_url = base_url + f'{dataset_path}/{args.compression}/{args.type}/'\n",
" dataset_output_path = join(args.output_path, dataset_path, args.compression, args.type)\n",
" \n",
" for filename in tqdm(filelist):\n",
" download_file(dataset_videos_url + filename + \".mp4\", join(dataset_output_path, filename + \".mp4\"))\n",
"\n",
"if __name__ == \"__main__\":\n",
" main()\n",
"'''\n",
"\n",
"with open(\"download_ffpp.py\", \"w\") as f:\n",
" f.write(download_script)\n",
"\n",
"!python download_ffpp.py ./data -d all -c c40 -t videos -n 50\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f33716f6",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import os\n",
"import glob\n",
"from tqdm import tqdm\n",
"\n",
"def extract_frames(video_folder, output_folder, label, max_frames=4):\n",
" os.makedirs(output_folder, exist_ok=True)\n",
" videos = glob.glob(os.path.join(video_folder, \"*.mp4\"))\n",
" \n",
" for vid_path in tqdm(videos, desc=f\"Extracting {label}\"):\n",
" vid_name = os.path.basename(vid_path).replace('.mp4','')\n",
" cap = cv2.VideoCapture(vid_path)\n",
" count = 0\n",
" while cap.isOpened() and count < max_frames:\n",
" ret, frame = cap.read()\n",
" if not ret: break\n",
" frame = cv2.resize(frame, (224, 224))\n",
" out_path = os.path.join(output_folder, f\"{vid_name}_f{count}.jpg\")\n",
" cv2.imwrite(out_path, frame)\n",
" count += 1\n",
" cap.release()\n",
"\n",
"# Extract Real\n",
"extract_frames('./data/original_sequences/youtube/c40/videos', './dataset/real', 'real')\n",
"\n",
"# Extract Fakes\n",
"fakes = ['Deepfakes', 'Face2Face', 'FaceSwap', 'NeuralTextures']\n",
"for f in fakes:\n",
" extract_frames(f'./data/manipulated_sequences/{f}/c40/videos', './dataset/fake', 'fake')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b79cdd85",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from datasets import load_dataset\n",
"from transformers import ViTImageProcessor, ViTForImageClassification, TrainingArguments, Trainer\n",
"import torch\n",
"\n",
"# 1. Load Dataset\n",
"dataset = load_dataset('imagefolder', data_dir='./dataset')\n",
"# Split into train/validation\n",
"dataset = dataset['train'].train_test_split(test_size=0.1)\n",
"\n",
"# 2. Preprocessor\n",
"model_name_or_path = 'google/vit-base-patch16-224-in21k'\n",
"processor = ViTImageProcessor.from_pretrained(model_name_or_path)\n",
"\n",
"def transform(example_batch):\n",
" # Take a list of PIL images and turn them to pixel values\n",
" inputs = processor([x.convert(\"RGB\") for x in example_batch['image']], return_tensors='pt')\n",
" inputs['labels'] = example_batch['label']\n",
" return inputs\n",
"\n",
"prepared_ds = dataset.with_transform(transform)\n",
"\n",
"def collate_fn(batch):\n",
" return {\n",
" 'pixel_values': torch.stack([x['pixel_values'] for x in batch]),\n",
" 'labels': torch.tensor([x['labels'] for x in batch])\n",
" }\n",
"\n",
"# 3. Load Model\n",
"labels = dataset['train'].features['label'].names\n",
"model = ViTForImageClassification.from_pretrained(\n",
" model_name_or_path,\n",
" num_labels=len(labels),\n",
" id2label={str(i): c for i, c in enumerate(labels)},\n",
" label2id={c: str(i) for i, c in enumerate(labels)}\n",
")\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./vit-deepshield\",\n",
" per_device_train_batch_size=16,\n",
" eval_strategy=\"steps\",\n",
" num_train_epochs=3,\n",
" fp16=True, # Mixed precision for speed\n",
" save_steps=100,\n",
" eval_steps=100,\n",
" logging_steps=10,\n",
" learning_rate=2e-4,\n",
" save_total_limit=2,\n",
" remove_unused_columns=False,\n",
" push_to_hub=False,\n",
" load_best_model_at_end=True,\n",
")\n",
"\n",
"import evaluate\n",
"metric = evaluate.load(\"accuracy\")\n",
"def compute_metrics(p):\n",
" return metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=collate_fn,\n",
" compute_metrics=compute_metrics,\n",
" train_dataset=prepared_ds[\"train\"],\n",
" eval_dataset=prepared_ds[\"test\"],\n",
")\n",
"\n",
"# 4. Train\n",
"train_results = trainer.train()\n",
"trainer.save_model(\"deepshield_vit_model\")\n",
"processor.save_pretrained(\"deepshield_vit_model\")\n",
"trainer.log_metrics(\"train\", train_results.metrics)\n",
"trainer.save_metrics(\"train\", train_results.metrics)\n",
"trainer.save_state()\n",
"print(\"Training Complete! The model is saved to ./deepshield_vit_model\")\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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