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import sys
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import os
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os.system(f'pip install dlib')
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import dlib
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import argparse
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import numpy as np
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from PIL import Image
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import cv2
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import torch
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from huggingface_hub import hf_hub_download
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import gradio as gr
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import models_vit
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from util.datasets import build_dataset
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from engine_finetune import test_two_class, test_multi_class
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import matplotlib.pyplot as plt
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from torchvision import transforms
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import traceback
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from pytorch_grad_cam import (
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GradCAM,ScoreCAM,
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XGradCAM, EigenCAM
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)
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from pytorch_grad_cam import GuidedBackpropReLUModel
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from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image
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def reshape_transform(tensor,height=14,width=14):
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result = tensor[:, 1:, :].reshape(tensor.size(0),height,width,tensor.size(2))
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result = result.transpose(2,3).transpose(1,2)
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return result
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def get_args_parser():
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parser = argparse.ArgumentParser('FSFM3C fine-tuning&Testing for image classification', add_help=False)
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parser.add_argument('--batch_size', default=64, type=int, help='Batch size per GPU')
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parser.add_argument('--epochs', default=50, type=int)
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parser.add_argument('--accum_iter', default=1, type=int, help='Accumulate gradient iterations')
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parser.add_argument('--model', default='vit_large_patch16', type=str, metavar='MODEL', help='Name of model to train')
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parser.add_argument('--input_size', default=224, type=int, help='images input size')
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parser.add_argument('--normalize_from_IMN', action='store_true', help='cal mean and std from imagenet')
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parser.set_defaults(normalize_from_IMN=True)
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parser.add_argument('--apply_simple_augment', action='store_true', help='apply simple data augment')
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parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT', help='Drop path rate')
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parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM', help='Clip gradient norm')
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parser.add_argument('--weight_decay', type=float, default=0.05, help='weight decay')
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parser.add_argument('--lr', type=float, default=None, metavar='LR', help='learning rate')
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parser.add_argument('--blr', type=float, default=1e-3, metavar='LR', help='base learning rate')
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parser.add_argument('--layer_decay', type=float, default=0.75, help='layer-wise lr decay')
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parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR', help='lower lr bound')
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parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N', help='epochs to warmup LR')
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parser.add_argument('--color_jitter', type=float, default=None, metavar='PCT', help='Color jitter factor')
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parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME', help='Use AutoAugment policy')
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parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing')
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parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT', help='Random erase prob')
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parser.add_argument('--remode', type=str, default='pixel', help='Random erase mode')
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parser.add_argument('--recount', type=int, default=1, help='Random erase count')
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parser.add_argument('--resplit', action='store_true', default=False, help='Do not random erase first augmentation split')
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parser.add_argument('--mixup', type=float, default=0, help='mixup alpha')
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parser.add_argument('--cutmix', type=float, default=0, help='cutmix alpha')
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parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None, help='cutmix min/max ratio')
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parser.add_argument('--mixup_prob', type=float, default=1.0, help='Probability of performing mixup or cutmix')
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parser.add_argument('--mixup_switch_prob', type=float, default=0.5, help='Probability of switching to cutmix')
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parser.add_argument('--mixup_mode', type=str, default='batch', help='How to apply mixup/cutmix params')
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parser.add_argument('--finetune', default='', help='finetune from checkpoint')
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parser.add_argument('--global_pool', action='store_true')
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parser.set_defaults(global_pool=True)
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parser.add_argument('--cls_token', action='store_false', dest='global_pool', help='Use class token for classification')
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parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str, help='dataset path')
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parser.add_argument('--nb_classes', default=1000, type=int, help='number of the classification types')
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parser.add_argument('--output_dir', default='', help='path where to save')
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parser.add_argument('--log_dir', default='', help='path where to tensorboard log')
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parser.add_argument('--device', default='cuda', help='device to use for training / testing')
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parser.add_argument('--seed', default=0, type=int)
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parser.add_argument('--resume', default='', help='resume from checkpoint')
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parser.add_argument('--start_epoch', default=0, type=int, metavar='N', help='start epoch')
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parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
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parser.set_defaults(eval=True)
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parser.add_argument('--dist_eval', action='store_true', default=False, help='Enabling distributed evaluation')
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parser.add_argument('--num_workers', default=10, type=int)
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parser.add_argument('--pin_mem', action='store_true', help='Pin CPU memory in DataLoader')
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parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
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parser.set_defaults(pin_mem=True)
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parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
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parser.add_argument('--local_rank', default=-1, type=int)
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parser.add_argument('--dist_on_itp', action='store_true')
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parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
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return parser
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def load_model(select_skpt):
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global ckpt, device, model, checkpoint
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if select_skpt not in CKPT_NAME:
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return gr.update(), "Select a correct model"
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ckpt = select_skpt
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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args.nb_classes = CKPT_CLASS[ckpt]
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model = models_vit.__dict__[CKPT_MODEL[ckpt]](
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num_classes=args.nb_classes,
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drop_path_rate=args.drop_path,
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global_pool=args.global_pool,
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).to(device)
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args.resume = os.path.join(CKPT_SAVE_PATH, CKPT_PATH[ckpt])
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if os.path.isfile(args.resume) == False:
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hf_hub_download(local_dir=CKPT_SAVE_PATH,
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local_dir_use_symlinks=False,
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repo_id='Wolowolo/fsfm-3c',
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filename=CKPT_PATH[ckpt])
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args.resume = os.path.join(CKPT_SAVE_PATH, CKPT_PATH[ckpt])
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checkpoint = torch.load(args.resume, map_location=device)
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model.load_state_dict(checkpoint['model'], strict=False)
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model.eval()
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global cam
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cam = GradCAM(model = model,
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target_layers=[model.blocks[-1].norm1],
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reshape_transform=reshape_transform
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)
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return gr.update(), f"[Loaded Model Successfully:] {args.resume}] "
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def get_boundingbox(face, width, height, minsize=None):
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x1, y1, x2, y2 = face.left(), face.top(), face.right(), face.bottom()
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size_bb = int(max(x2 - x1, y2 - y1) * 1.3)
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if minsize and size_bb < minsize:
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size_bb = minsize
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center_x, center_y = (x1 + x2) // 2, (y1 + y2) // 2
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x1, y1 = max(int(center_x - size_bb // 2), 0), max(int(center_y - size_bb // 2), 0)
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size_bb = min(width - x1, size_bb)
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size_bb = min(height - y1, size_bb)
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return x1, y1, size_bb
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def extract_face(frame):
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face_detector = dlib.get_frontal_face_detector()
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image = np.array(frame.convert('RGB'))
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faces = face_detector(image, 1)
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if faces:
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face = faces[0]
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x, y, size = get_boundingbox(face, image.shape[1], image.shape[0])
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cropped_face = image[y:y + size, x:x + size]
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return Image.fromarray(cropped_face)
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return None
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def get_frame_index_uniform_sample(total_frame_num, extract_frame_num):
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return np.linspace(0, total_frame_num - 1, num=extract_frame_num, dtype=int).tolist()
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def extract_face_from_fixed_num_frames(src_video, dst_path, num_frames=None):
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video_capture = cv2.VideoCapture(src_video)
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total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
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frame_indices = get_frame_index_uniform_sample(total_frames, num_frames) if num_frames else range(total_frames)
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for frame_index in frame_indices:
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video_capture.set(cv2.CAP_PROP_POS_FRAMES, frame_index)
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ret, frame = video_capture.read()
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if not ret:
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continue
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image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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img = extract_face(image)
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if img:
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img = img.resize((224, 224), Image.BICUBIC)
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save_img_name = f"frame_{frame_index}.png"
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img.save(os.path.join(dst_path, '0', save_img_name))
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video_capture.release()
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return frame_indices
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class TargetCategory:
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def __init__(self, category_index):
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self.category_index = category_index
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def __call__(self, output):
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return output[self.category_index]
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def preprocess_image_cam(pil_img,mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225]):
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img_np = np.array(pil_img)
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img_np = img_np.astype(np.float32) / 255.0
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img_np = (img_np - mean) / std
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img_np = np.transpose(img_np, (2, 0, 1))
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img_np = np.expand_dims(img_np, axis=0)
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return img_np
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def FSFM3C_image_detection(image):
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frame_path = os.path.join(FRAME_SAVE_PATH, str(len(os.listdir(FRAME_SAVE_PATH))))
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os.makedirs(frame_path, exist_ok=True)
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os.makedirs(os.path.join(frame_path, '0'), exist_ok=True)
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img = extract_face(image)
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if img is None:
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return 'No face detected, please upload a clear face!'
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img = img.resize((224, 224), Image.BICUBIC)
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img.save(os.path.join(frame_path, '0', "frame_0.png"))
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args.data_path = frame_path
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args.batch_size = 1
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dataset_val = build_dataset(is_train=False, args=args)
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sampler_val = torch.utils.data.SequentialSampler(dataset_val)
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data_loader_val = torch.utils.data.DataLoader(dataset_val, sampler=sampler_val, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False)
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if CKPT_CLASS[ckpt] > 2:
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frame_preds_list, video_pred_list = test_multi_class(data_loader_val, model, device)
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class_names = ['Real or Bonafide', 'Deepfake', 'Diffusion or AIGC generated', 'Spoofing or Presentation-attack']
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avg_video_pred = np.mean(video_pred_list, axis=0)
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max_prob_index = np.argmax(avg_video_pred)
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max_prob_class = class_names[max_prob_index]
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probabilities = [f"{class_names[i]}: {prob * 100:.1f}%" for i, prob in enumerate(avg_video_pred)]
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image_results = f"The largest face in this image may be {max_prob_class} with probability: \n [{', '.join(probabilities)}]"
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use_cuda = True
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input_tensor = preprocess_image(img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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if use_cuda:
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input_tensor = input_tensor.cuda()
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category_names_to_index = {
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'Real or Bonafide': 0,
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'Deepfake': 1,
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'Diffusion or AIGC generated': 2,
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'Spoofing or Presentation-attack': 3
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}
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target_category = TargetCategory(category_names_to_index[max_prob_class])
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grayscale_cam = cam(input_tensor=input_tensor, targets=[target_category])
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grayscale_cam = 1 - grayscale_cam[0, :]
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img = np.array(img)
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if img.shape[2] == 4:
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img = img[:, :, :3]
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img = img.astype(np.float32) / 255.0
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visualization = show_cam_on_image(img, grayscale_cam)
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visualization = cv2.cvtColor(visualization, cv2.COLOR_RGB2BGR)
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output_path = "./CAM_images/output_heatmap.png"
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cv2.imwrite(output_path, visualization)
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return image_results, output_path,probabilities[max_prob_index]
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if CKPT_CLASS[ckpt] == 2:
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frame_preds_list, video_pred_list = test_two_class(data_loader_val, model, device)
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if ckpt == 'DfD-Checkpoint_Fine-tuned_on_FF++':
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prob = sum(video_pred_list) / len(video_pred_list)
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label = "Deepfake" if prob <= 0.5 else "Real"
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prob = prob if label == "Real" else 1 - prob
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if ckpt == 'FAS-Checkpoint_Fine-tuned_on_MCIO':
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prob = sum(video_pred_list) / len(video_pred_list)
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label = "Spoofing" if prob <= 0.5 else "Bonafide"
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prob = prob if label == "Bonafide" else 1 - prob
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image_results = f"The largest face in this image may be {label} with probability {prob * 100:.1f}%"
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return image_results, None ,None
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def FSFM3C_video_detection(video, num_frames):
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try:
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frame_path = os.path.join(FRAME_SAVE_PATH, str(len(os.listdir(FRAME_SAVE_PATH))))
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os.makedirs(frame_path, exist_ok=True)
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os.makedirs(os.path.join(frame_path, '0'), exist_ok=True)
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frame_indices = extract_face_from_fixed_num_frames(video, frame_path, num_frames=num_frames)
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args.data_path = frame_path
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args.batch_size = num_frames
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dataset_val = build_dataset(is_train=False, args=args)
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sampler_val = torch.utils.data.SequentialSampler(dataset_val)
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data_loader_val = torch.utils.data.DataLoader(dataset_val, sampler=sampler_val, batch_size=args.batch_size, num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=False)
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if CKPT_CLASS[ckpt] > 2:
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frame_preds_list, video_pred_list = test_multi_class(data_loader_val, model, device)
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class_names = ['Real or Bonafide', 'Deepfake', 'Diffusion or AIGC generated', 'Spoofing or Presentation-attack']
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avg_video_pred = np.mean(video_pred_list, axis=0)
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max_prob_index = np.argmax(avg_video_pred)
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max_prob_class = class_names[max_prob_index]
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probabilities = [f"{class_names[i]}: {prob * 100:.1f}%" for i, prob in enumerate(avg_video_pred)]
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frame_results = {f"frame_{frame_indices[i]}": [f"{class_names[j]}: {prob * 100:.1f}%" for j, prob in enumerate(frame_preds_list[i])] for i in range(len(frame_indices))}
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video_results = (f"The largest face in this image may be {max_prob_class} with probability: \n [{', '.join(probabilities)}]\n \n"
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f"The frame-level detection results ['frame_index': 'probabilities']: \n{frame_results}")
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return video_results
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if CKPT_CLASS[ckpt] == 2:
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frame_preds_list, video_pred_list = test_two_class(data_loader_val, model, device)
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if ckpt == 'DfD-Checkpoint_Fine-tuned_on_FF++':
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prob = sum(video_pred_list) / len(video_pred_list)
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label = "Deepfake" if prob <= 0.5 else "Real"
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prob = prob if label == "Real" else 1 - prob
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frame_results = {f"frame_{frame_indices[i]}": f"{(frame_preds_list[i]) * 100:.1f}%" for i in
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range(len(frame_indices))} if label == "Real" else {f"frame_{frame_indices[i]}": f"{(1 - frame_preds_list[i]) * 100:.1f}%" for i in
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range(len(frame_indices))}
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if ckpt == 'FAS-Checkpoint_Fine-tuned_on_MCIO':
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prob = sum(video_pred_list) / len(video_pred_list)
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label = "Spoofing" if prob <= 0.5 else "Bonafide"
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prob = prob if label == "Bonafide" else 1 - prob
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frame_results = {f"frame_{frame_indices[i]}": f"{(frame_preds_list[i]) * 100:.1f}%" for i in
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range(len(frame_indices))} if label == "Bonafide" else {f"frame_{frame_indices[i]}": f"{(1 - frame_preds_list[i]) * 100:.1f}%" for i in
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range(len(frame_indices))}
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video_results = (f"The largest face in this image may be {label} with probability {prob * 100:.1f}%\n \n"
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f"The frame-level detection results ['frame_index': 'real_face_probability']: \n{frame_results}")
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return video_results
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except Exception as e:
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return f"Error occurred. Please provide a clear face video or reduce the number of frames."
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P = os.path.abspath(__file__)
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FRAME_SAVE_PATH = os.path.join(os.path.dirname(P), 'frame')
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CKPT_SAVE_PATH = os.path.join(os.path.dirname(P), 'checkpoints')
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os.makedirs(FRAME_SAVE_PATH, exist_ok=True)
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os.makedirs(CKPT_SAVE_PATH, exist_ok=True)
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CKPT_NAME = [
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'✨Unified-detector_v1_Fine-tuned_on_4_classes',
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'DfD-Checkpoint_Fine-tuned_on_FF++',
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'FAS-Checkpoint_Fine-tuned_on_MCIO',
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]
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CKPT_PATH = {
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'✨Unified-detector_v1_Fine-tuned_on_4_classes': 'finetuned_models/Unified-detector/v1_Fine-tuned_on_4_classes/checkpoint-min_train_loss.pth',
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'DfD-Checkpoint_Fine-tuned_on_FF++': 'finetuned_models/FF++_c23_32frames/checkpoint-min_val_loss.pth',
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'FAS-Checkpoint_Fine-tuned_on_MCIO': 'finetuned_models/MCIO_protocol/Both_MCIO/checkpoint-min_val_loss.pth',
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}
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CKPT_CLASS = {
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'✨Unified-detector_v1_Fine-tuned_on_4_classes': 4,
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'DfD-Checkpoint_Fine-tuned_on_FF++': 2,
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'FAS-Checkpoint_Fine-tuned_on_MCIO': 2
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}
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CKPT_MODEL = {
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'✨Unified-detector_v1_Fine-tuned_on_4_classes': 'vit_base_patch16',
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'DfD-Checkpoint_Fine-tuned_on_FF++': 'vit_base_patch16',
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'FAS-Checkpoint_Fine-tuned_on_MCIO': 'vit_base_patch16',
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}
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with gr.Blocks(css=".custom-label { font-weight: bold !important; font-size: 16px !important; }") as demo:
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gr.HTML("<h1 style='text-align: center;'>🦱 Real Facial Image&Video Detection <br> Against Face Forgery (Deepfake/Diffusion) and Spoofing (Presentation-attacks)</h1>")
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gr.Markdown("<b>☉ Powered by the fine-tuned ViT models that is pre-trained from [FSFM-3C](https://fsfm-3c.github.io/)</b> <br> "
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"<b>☉ We do not and cannot access or store the data you have uploaded!</b> <br> "
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"<b>☉ Release (Continuously updating) </b> <br> <b>[V1.0] 2025/02/22-Current🎉</b>: "
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"1) Updated <b>[✨Unified-detector_v1] for Unified Physical-Digital Face Attack&Forgery Detection, a ViT-B/16-224 (FSFM Pre-trained) detector that could identify Real&Bonafide, Deepfake, Diffusion&AIGC, Spooing&Presentation-attacks facial images or videos </b> ; 2) Provided the selection of the number of video frames (uniformly sampling 1-32 frames, more frames may time-consuming for this page without GPU acceleration); 3) Fixed some errors of V0.1 including loading and prediction. <br>"
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"<b>[V0.1] 2024/12-2025/02/21</b>: "
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"Create this page with basic detectors [DfD-Checkpoint_Fine-tuned_on_FF++, FAS-Checkpoint_Fine-tuned_on_MCIO] that follow the paper implementation. <br> ")
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gr.Markdown("- Please <b>provide a facial image or video(<100s)</b>, and <b>select the model</b> for detection: <br> <b>[SUGGEST] [✨Unified-detector_v1_Fine-tuned_on_4_classes]</b> a (FSFM Pre-trained) ViT-B/16-224 for Both Real/Deepfake/Diffusion/Spoofing facial images&videos Detection <br> <b>[DfD-Checkpoint_Fine-tuned_on_FF++]</b> for deepfake detection, FSFM ViT-B/16-224 fine-tuned on the FF++_c23 train&val sets (4 manipulations, 32 frames per video) <br> <b>[FAS-Checkpoint_Fine-tuned_on_MCIO]</b> for face anti-spoofing, FSFM ViT-B/16-224 fine-tuned on the MCIO datasets (2 frames per video)")
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with gr.Row():
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ckpt_select_dropdown = gr.Dropdown(
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label="Select the Model for Detection ⬇️",
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elem_classes="custom-label",
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choices=['Choose Model Here 🖱️'] + CKPT_NAME + ['continuously updating...'],
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multiselect=False,
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value='Choose Model Here 🖱️',
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interactive=True,
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)
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model_loading_status = gr.Textbox(label="Model Loading Status")
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with gr.Row():
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with gr.Column(scale=5):
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gr.Markdown("### Image Detection (Fast Try: copying image from [whichfaceisreal](https://whichfaceisreal.com/))")
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image = gr.Image(label="Upload/Capture/Paste your image", type="pil")
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image_submit_btn = gr.Button("Submit")
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output_results_image = gr.Textbox(label="Detection Result")
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with gr.Row():
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output_heatmap = gr.Image(label="Grad_CAM")
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output_max_prob_class = gr.Textbox(label="Detected Class")
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with gr.Column(scale=5):
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gr.Markdown("### Video Detection")
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video = gr.Video(label="Upload/Capture your video")
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frame_slider = gr.Slider(minimum=1, maximum=32, step=1, value=32, label="Number of Frames for Detection")
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video_submit_btn = gr.Button("Submit")
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output_results_video = gr.Textbox(label="Detection Result")
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ckpt_select_dropdown.change(
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fn=load_model,
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inputs=[ckpt_select_dropdown],
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outputs=[ckpt_select_dropdown, model_loading_status],
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)
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image_submit_btn.click(
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fn=FSFM3C_image_detection,
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inputs=[image],
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outputs=[output_results_image, output_heatmap,output_max_prob_class],
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)
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video_submit_btn.click(
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fn=FSFM3C_video_detection,
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inputs=[video, frame_slider],
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outputs=[output_results_video],
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)
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if __name__ == "__main__":
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args = get_args_parser()
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args = args.parse_args()
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ckpt = '✨Unified-detector_v1_Fine-tuned_on_4_classes'
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|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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args.nb_classes = CKPT_CLASS[ckpt]
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model = models_vit.__dict__[CKPT_MODEL[ckpt]](
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num_classes=args.nb_classes,
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drop_path_rate=args.drop_path,
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global_pool=args.global_pool,
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).to(device)
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args.resume = os.path.join(CKPT_SAVE_PATH, CKPT_PATH[ckpt])
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if os.path.isfile(args.resume) == False:
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hf_hub_download(local_dir=CKPT_SAVE_PATH,
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|
local_dir_use_symlinks=False,
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repo_id='Wolowolo/fsfm-3c',
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|
filename=CKPT_PATH[ckpt])
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|
args.resume = os.path.join(CKPT_SAVE_PATH, CKPT_PATH[ckpt])
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|
checkpoint = torch.load(args.resume, map_location=device)
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|
|
model.load_state_dict(checkpoint['model'], strict=False)
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model.eval()
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gr.close_all()
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demo.queue()
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demo.launch() |
