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import gradio as gr
import torch
from PIL import Image
import numpy as np
from util.feature_extraction_utils import normalize_transforms
from util.attack_utils import Attack
from util.prepare_utils import prepare_models, prepare_dir_vec, get_ensemble
from align.detector import detect_faces
from align.align_trans import get_reference_facial_points, warp_and_crop_face
import torchvision.transforms as transforms
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
to_tensor = transforms.ToTensor()
eps = 0.05
n_iters = 50
input_size = [112, 112]
attack_type = 'lpips'
c_tv = None
c_sim = 0.05
lr = 0.0025
net_type = 'alex'
noise_size = 0.005
n_starts = 1
kernel_size_gf = 7
sigma_gf = 3
combination = True
using_subspace = False
V_reduction_root = './'
model_backbones = ['IR_152', 'IR_152', 'ResNet_152', 'ResNet_152']
model_roots = ['models/Backbone_IR_152_Arcface_Epoch_112.pth', 'models/Backbone_IR_152_Cosface_Epoch_70.pth', \
'models/Backbone_ResNet_152_Arcface_Epoch_65.pth', 'models/Backbone_ResNet_152_Cosface_Epoch_68.pth']
direction = 1
crop_size = 112
scale = crop_size / 112.
models_attack, V_reduction, dim = prepare_models(model_backbones,
input_size,
model_roots,
kernel_size_gf,
sigma_gf,
combination,
using_subspace,
V_reduction_root)
def protect(img):
img = Image.fromarray(img)
reference = get_reference_facial_points(default_square=True) * scale
h,w,c = np.array(img).shape
_, landmarks = detect_faces(img)
facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)]
_, tfm = warp_and_crop_face(np.array(img), facial5points, reference, crop_size=(crop_size, crop_size))
# pytorch transform
theta = normalize_transforms(tfm, w, h)
tensor_img = to_tensor(img).unsqueeze(0).to(device)
V_reduction = None
dim = 512
# Find gradient direction vector
dir_vec_extractor = get_ensemble(models = models_attack, sigma_gf=None, kernel_size_gf=None, combination=False, V_reduction=V_reduction, warp=True, theta_warp=theta)
dir_vec = prepare_dir_vec(dir_vec_extractor, tensor_img, dim, combination)
img_attacked = tensor_img.clone()
attack = Attack(models_attack, dim, attack_type,
eps, c_sim, net_type, lr, n_iters,
noise_size, n_starts, c_tv, sigma_gf,
kernel_size_gf, combination, warp=True,
theta_warp=theta, V_reduction=V_reduction)
img_attacked = attack.execute(tensor_img, dir_vec, direction).detach().cpu()
img_attacked_pil = transforms.ToPILImage()(img_attacked[0])
return img_attacked_pil
gr.Interface(fn=protect, inputs=gr.components.Image(shape=(512,512)),
outputs=gr.components.Image(type="pil"), allow_flagging="never").launch(show_error=True, quiet=False, share=True)
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