├── FaceOff ├── __init__.py └── AFR.py ├── .gitignore ├── examples ├── procedure.png ├── faces │ ├── input │ │ ├── john.jpg │ │ ├── heath.jpg │ │ ├── meryl.jpg │ │ ├── ronald.jpg │ │ └── winona.jpg │ ├── target │ │ ├── chris.jpg │ │ ├── emma.jpg │ │ ├── halle.jpg │ │ ├── keanu.jpg │ │ └── nick.jpg │ ├── delta-example.png │ ├── input-face-example.png │ ├── target-face-example.png │ └── combined-face-example.png └── example.py ├── rlencevicius_poster.pdf ├── requirements.txt ├── setup.py ├── README.md └── LICENSE /FaceOff/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | results/ 2 | __pycache__ 3 | .vscode/ -------------------------------------------------------------------------------- /examples/procedure.png: 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-------------------------------------------------------------------------------- /examples/example.py: -------------------------------------------------------------------------------- 1 | from FaceOff.AFR import load_data, Attack 2 | from PIL import Image 3 | 4 | # Load the data. This will detect and resize the faces 5 | inputs = load_data('./faces/input/') 6 | targets = load_data('./faces/target/') 7 | 8 | # Initialize the Attack object with 9 | adversarial = Attack(inputs[0], targets[3], optimizer='adam') 10 | 11 | # Perform adversarial training 12 | adversarial_tensor, mask_tensor, img = adversarial.train(detect=True, verbose=True) 13 | 14 | # Show the image with mask applied 15 | img.show() -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup( 4 | name='FaceOff', 5 | version='0.0.0', 6 | description='Steps towards physical adversarial attacks on facial recognition', 7 | url='https://github.com/392781/FaceOff', 8 | author='Ronald Lencevičius', 9 | license='GNU General Public License v3 (GPLv3)', 10 | packages=['FaceOff'], 11 | install_requires=['torch>=1.13.1', 12 | 'torchvision==0.8.2', 13 | 'facenet-pytorch==2.5.1', 14 | 'face_recognition', 15 | 'face_recognition_models', 16 | 'cmake', 17 | 'dlib', 18 | 'numpy', 19 | 'Pillow' 20 | ], 21 | 22 | classifiers=[ 23 | 'Intended Audience :: Science/Research', 24 | 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)', 25 | 'Operating System :: POSIX :: Linux', 26 | 'Programming Language :: Python :: 3', 27 | 'Programming Language :: Python :: 3.8', 28 | 'Topic :: Scientific/Engineering :: Artificial Intelligence', 29 | 'Topic :: Scientific/Engineering :: Image Recognition', 30 | ], 31 | ) 32 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FaceOff 2 | ### Steps towards physical adversarial attacks on facial recognition 3 | 4 |

5 | 6 | 7 | 8 | 9 |

10 | 11 | Input image on the left is detected as the target image on the right after the mask has been applied. 12 | 13 | [![CC BY-SA 4.0][cc-by-sa-shield]][cc-by-sa] 14 | 15 | This work is licensed under a 16 | [Creative Commons Attribution-ShareAlike 4.0 International License][cc-by-sa]. 17 | 18 | [![CC BY-SA 4.0][cc-by-sa-image]][cc-by-sa] 19 | 20 | [cc-by-sa]: http://creativecommons.org/licenses/by-sa/4.0/ 21 | [cc-by-sa-image]: https://licensebuttons.net/l/by-sa/4.0/88x31.png 22 | [cc-by-sa-shield]: https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg 23 | 24 | ## Table of contents 25 | 26 | * [Table of contents](#table-of-contents) 27 | * [Description](#description) 28 | * [Installation](#installation) 29 | + [Requirements](#requirements) 30 | + [Instructions](#instructions) 31 | * [Citation](#citation) 32 | * [References](#references) 33 | 34 | ## Description 35 | The purpose of this library is to create adversarial attacks against the FaceNet face recognizer. This is the preliminary work towards creating a more robust physical attack using a mask that a person could wear over their face. 36 | 37 | For more details, please check out my [research poster](https://github.com/392781/FaceOff/blob/master/rlencevicius_poster.pdf). 38 | 39 | The current pipeline consists of an aligned input image with a calculated mask. This is then fed into a face detector using dlib's histogram of oriented gradients detector to test whether the face is still detected. This is then passed to FaceNet where which ouputs a face embedding and a loss which is then calculated and propagated back. This perturbs the input mask which generates enough of a disturbance to affect the loss. 40 | 41 | The loss function maximizes the Euclidean distance between the inputs' true identity and minimizes the distance between the adversarial input and the target image. 42 | 43 | An image of this process can be seen below. 44 | 45 | 46 |

47 | 48 |

49 | 50 | ## Installation 51 | 52 | ### Requirements 53 | 54 | This project works on Linux (Ubuntu 20.04). Windows and Mac are not supported but may work. 55 | 56 | ### Instructions 57 | 58 | 1. Create a virtual environment 59 | 60 | ```bash 61 | conda create -n facial_recognition python=3.8.5 62 | conda activate facial_recognition 63 | ``` 64 | 65 | 2. Clone the repo 66 | 67 | ```git 68 | git clone https://github.com/392781/FaceOff.git 69 | ``` 70 | 71 | 3. Install the required libraries 72 | 73 | ```bash 74 | pip install -r requirements.txt 75 | ``` 76 | 77 | 4. Install FaceOff from inside the folder where `setup.py` is located 78 | 79 | ```bash 80 | pip install -e . 81 | ``` 82 | 83 | 5. Import and use! 84 | 85 | ```python 86 | from FaceOff.AFR import load_data, Attack 87 | ``` 88 | 89 | For training instructions look at [`example.py`](https://github.com/392781/FaceOff/blob/master/examples/example.py) to get started in less than 30 lines. 90 | 91 | ## Citation 92 | Please cite `FaceOff` if used in your research: 93 | 94 | ```tex 95 | @misc{FaceOff, 96 | author = {Ronaldas Paulius Lencevicius}, 97 | howpublished = {GitHub}, 98 | title = {Face-Off: Steps towards physical adversarial attacks on facial recognition}, 99 | URL = {https://github.com/392781/FaceOff}, 100 | month = {Aug} 101 | year = {2019}, 102 | } 103 | ``` 104 | 105 | ## References 106 | * Sharif, Mahmood, et al. "Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition." Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016. 107 | * Wang, Mei, and Weihong Deng. "Deep face recognition: A survey." arXiv preprint arXiv:1804.06655 (2018). 108 | * MacDonald, Bruce. “Fooling Facial Detection with Fashion.” Towards Data Science, Towards Data Science, 4 June 2019, towardsdatascience.com/fooling-facial-detection-with-fashion-d668ed919eb. 109 | * Thys, Simen, et al. "Fooling automated surveillance cameras: adversarial patches to attack person detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019. 110 | 111 | Used the [PyTorch FaceNet implementation](https://github.com/timesler/facenet-pytorch) by Tim Esler 112 | -------------------------------------------------------------------------------- /FaceOff/AFR.py: -------------------------------------------------------------------------------- 1 | import os 2 | import re 3 | import dlib 4 | import face_recognition as fr 5 | import face_recognition_models as frm 6 | import numpy as np 7 | import torch as t 8 | from torchvision.transforms import Compose, Normalize, ToTensor, ToPILImage 9 | from facenet_pytorch import InceptionResnetV1 10 | from PIL import Image, ImageDraw, ImageChops 11 | 12 | device = t.device('cuda' if t.cuda.is_available() else 'cpu') 13 | 14 | class Attack(object): 15 | """ 16 | Class used to create adversarial facial recognition attacks 17 | """ 18 | def __init__(self, 19 | input_img, 20 | target_img, 21 | seed=None, 22 | optimizer='sgd', 23 | lr = 1e-1, 24 | pretrained='vggface2' 25 | ): 26 | """ 27 | Initialization for Attack class. Attack contains the following: 28 | input_tensor 29 | input_emb 30 | target_emb 31 | mask_tensor 32 | ref (mask reference for _apply) 33 | 34 | Parameters 35 | ---------- 36 | input_img : PIL.Image 37 | Image to train on. 38 | 39 | target_img : PIL.Image 40 | Image to target the adversarial attack against. 41 | 42 | seed : int, optional 43 | Sets custom seed for reproducability. Default is generated randomly. 44 | 45 | optimizer : str, optional 46 | Takes in either 'sgd', 'adam', or 'adamax'. Default is 'adam'. 47 | 48 | lr : float, optional 49 | Learning rate. Default is 1e-1 or 0.1. 50 | 51 | pretrained : str, optional 52 | Pretrained weights for FaceNet. Options are 'vggface2' or 'casia-webface'. 53 | Default is 'vggface2'. 54 | """ 55 | # Value inits 56 | if (seed != None) : np.random.seed(seed) 57 | self.MEAN = t.tensor([0.485, 0.456, 0.406], device=device) 58 | self.STD = t.tensor([0.229, 0.224, 0.225], device=device) 59 | self.LOSS = t.tensor(0, device=device) 60 | 61 | # Function inits 62 | self.imageize = ToPILImage() 63 | self.tensorize = ToTensor() 64 | self.normalize = Normalize(mean=self.MEAN.cpu().numpy(), std=self.STD.cpu().numpy()) 65 | self.resnet = InceptionResnetV1(pretrained=pretrained).eval().to(device) 66 | 67 | # Training inputs 68 | # Original image - normalized and with embedding created. 69 | self.input_tensor = self.normalize(self.tensorize(input_img).to(device)) 70 | self.input_emb = self.resnet( 71 | t.stack([ 72 | self.input_tensor 73 | ]) 74 | ) 75 | # Target image - normalized and with embedding created. 76 | self.target_emb = self.resnet( 77 | t.stack([ 78 | self.normalize( 79 | self.tensorize(target_img).to(device) 80 | ) 81 | ]) 82 | ) 83 | # Adversarial embedding init 84 | self.adversarial_emb = None 85 | # Face mask init 86 | self.mask_tensor = self._create_mask(input_img) 87 | # Reference tensor used to apply mask 88 | self.ref = self.mask_tensor 89 | 90 | # Optimizer init 91 | try: 92 | if (optimizer == 'sgd'): 93 | self.opt = t.optim.SGD([self.mask_tensor], lr = lr, momentum = 0.9, weight_decay = 0.0001) 94 | elif (optimizer == 'adam'): 95 | self.opt = t.optim.Adam([self.mask_tensor], lr = lr, weight_decay = 0.0001) 96 | elif (optimizer == 'adamax'): 97 | self.opt = t.optim.Adamax([self.mask_tensor], lr = lr, weight_decay = 0.0001) 98 | except: 99 | print("Optimizer not supported, reverting to ADAM") 100 | self.opt = t.optim.Adam([self.mask_tensor], lr = lr, weight_decay = 0.0001) 101 | 102 | 103 | def train(self, 104 | epochs = 30, 105 | detect=False, 106 | verbose=False 107 | ): 108 | """ 109 | Adversarial training process for facial recognition. 110 | 111 | Parameters 112 | ---------- 113 | epochs : int, optional 114 | Number of training epochs. Default is 30. 115 | 116 | detect : bool, optional 117 | Perform facial detection during training process and log result. Default is False. 118 | 119 | verbose : bool, optional 120 | Output full embedding distance information during training. Default is False. 121 | 122 | Returns 123 | ------- 124 | list 125 | Adversarial tensor, mask tensor, adversarial image 126 | """ 127 | for i in range(1, epochs + 1): 128 | # Create adversarial tensor by applying normalized MASK to normalized INPUT 129 | self.view(self.mask_tensor).show() 130 | adversarial_tensor = self._apply( 131 | self.input_tensor, 132 | self.normalize(self.mask_tensor), 133 | self.ref) 134 | # Create embedding 135 | self.adversarial_emb = self.resnet(t.stack([adversarial_tensor])) 136 | 137 | # Calculate two distances - from adv to input and adv to target 138 | distance_to_image = self._emb_distance(self.adversarial_emb, self.input_emb) 139 | distance_to_target = self._emb_distance(self.adversarial_emb, self.target_emb) 140 | # Calculate loss - maximize distance to image, minimize distance to target 141 | self.LOSS = (-distance_to_image + distance_to_target) 142 | # Adversarially backpropagate the information back to original adversarial_tensor 143 | self.LOSS.backward(retain_graph=True) 144 | # Update optimizer and zero out gradients 145 | self.opt.step() 146 | self.opt.zero_grad() 147 | # Allow the updated mask to have only values (0, 255) or normalized (0, 1) 148 | self.mask_tensor.data.clamp_(0, 1) 149 | 150 | # Various logging information 151 | training_information = [f'Epoch {i}: \n Loss = {self.LOSS.item():.7f}'] 152 | if verbose: 153 | training_information.append(f'\n Dist. to Image = {distance_to_image:.7f}') 154 | training_information.append(f'\n Dist. to Target = {distance_to_target:.7f}') 155 | if detect: 156 | face_loc = fr.face_locations(np.array(self.imageize(self._reverse_norm(adversarial_tensor).detach()))) 157 | detected = False if not face_loc else True 158 | training_information.append(f'\n Face detection = {detected}') 159 | print(''.join(training_information)) 160 | 161 | # Return original adversarial tensor, the adversarial image, and the mask tensor 162 | adversarial_image = self.imageize(self._reverse_norm(adversarial_tensor).detach()) 163 | return adversarial_tensor, self.mask_tensor, adversarial_image 164 | 165 | 166 | def view(self, 167 | norm_image_tensor, 168 | norm_mask_tensor=None 169 | ): 170 | """ 171 | Preview a tensor as an image 172 | 173 | Parameters 174 | ---------- 175 | norm_image_tensor : torch.Tensor 176 | Image to convert. 177 | norm_mask_tensor : torch.Tensor, optional 178 | Mask to apply to image. Default is None. 179 | 180 | Returns 181 | ------- 182 | PIL.Image 183 | """ 184 | if norm_mask_tensor is not None: 185 | combined_tensor = self._apply(norm_image_tensor, norm_mask_tensor, self.ref) 186 | return self.imageize(self._reverse_norm(combined_tensor).detach()) 187 | else: 188 | return self.imageize(self._reverse_norm(norm_image_tensor).detach()) 189 | 190 | 191 | def _apply(self, 192 | image_tensor, 193 | mask_tensor, 194 | reference_tensor 195 | ): 196 | """ 197 | Apply a mask over an image. 198 | 199 | Parameters 200 | ---------- 201 | image_tensor : torch.Tensor 202 | Canvas to be used to apply mask on. 203 | 204 | mask_tensor : torch.Tensor 205 | Mask to apply over the image. 206 | 207 | reference_tensor : torch.Tensor 208 | Used to reference mask boundaries 209 | 210 | Returns 211 | ------- 212 | torch.Tensor 213 | """ 214 | return t.where((reference_tensor == 0), image_tensor, mask_tensor).to(device) 215 | 216 | 217 | def _create_mask(self, face_image): 218 | """ 219 | Helper function to create a facial mask to cover lower portion of the 220 | face. Uses 'face_recognizer' library's landmark detector to build a 221 | list of tuples containing (x, y) coordinates of the lower chin area as 222 | well as the middle of the nose tip. 223 | 224 | A polygon is then drawn using those tuples creating a "taco" shaped 225 | face mask. This is then processed for each channel with a value of 226 | 0 for white areas and a value of 1 for black areas (the taco area) 227 | 228 | This will later be used as a tensor that takes in these given values 229 | 230 | 231 | Parameters 232 | ---------- 233 | face_image : PIL.Image 234 | image of a detected face 235 | 236 | Returns 237 | ------- 238 | mask_tensor : torch.Tensor 239 | mask tensor 240 | """ 241 | # New image mask 242 | mask = Image.new('RGB', face_image.size, color=(0,0,0)) 243 | # Draw on mask 244 | d = ImageDraw.Draw(mask) 245 | 246 | # Detect face landmarks 247 | landmarks = fr.face_landmarks(np.array(face_image)) 248 | # Gross list comprehension magic to add coordinates from image to create face mask 249 | area = [landmark 250 | for landmark in landmarks[0]['chin'] 251 | if landmark[1] > max(landmarks[0]['nose_tip'])[1]] 252 | area.append(landmarks[0]['nose_bridge'][1]) 253 | 254 | # Fill the new face mask area 255 | d.polygon(area, fill=(255,255,255)) 256 | # Create a numpy array and set type as floating point (used in training) 257 | mask_array = np.array(mask) 258 | mask_array = mask_array.astype(np.float32) 259 | 260 | # Fill the mask in with the color grey 261 | # C 262 | for i in range(mask_array.shape[0]): 263 | # C 264 | for j in range(mask_array.shape[1]): 265 | # C 266 | for k in range(mask_array.shape[2]): 267 | # Combo BREAKER 268 | if mask_array[i][j][k] == 255.: 269 | mask_array[i][j][k] = 0.5 270 | else: 271 | mask_array[i][j][k] = 0 272 | 273 | # Create the mask tensor and initialize gradient 274 | mask_tensor = ToTensor()(mask_array).to(device) 275 | mask_tensor.requires_grad_(True) 276 | 277 | return mask_tensor 278 | 279 | 280 | def _emb_distance(self, tensor_1, tensor_2): 281 | """ 282 | Helper function to calculate Euclidean distance between two tensors. 283 | 284 | Parameters 285 | ---------- 286 | tensor_1, tensor_2 : torch.Tensor 287 | Tensors used for distance calculation 288 | 289 | Returns 290 | distance_tensor : torch.Tensor 291 | Tensor containing distance value 292 | ------- 293 | 294 | """ 295 | distance_tensor = (tensor_1 - tensor_2).norm() 296 | return distance_tensor 297 | 298 | 299 | def _reverse_norm(self, image_tensor): 300 | """ 301 | Reverses normalization for a given image_tensor 302 | 303 | Parameters 304 | ---------- 305 | image_tensor : torch.Tensor 306 | 307 | Returns 308 | ------- 309 | torch.Tensor 310 | """ 311 | return image_tensor * self.STD[:, None, None] + self.MEAN[:, None, None] 312 | 313 | 314 | 315 | def detect_face(image_loc): 316 | """ 317 | Helper function to run the facial detection and alignment process using 318 | dlib. Detects a given face and aligns it using dlib's 5 point landmark 319 | detector. 320 | 321 | Parameters 322 | ---------- 323 | image_loc : numpy.array 324 | image file location 325 | 326 | Returns 327 | ------- 328 | face_image : PIL.Image 329 | Resized face image 330 | """ 331 | detector = dlib.get_frontal_face_detector() 332 | shape_predictor = dlib.shape_predictor(frm.pose_predictor_model_location()) 333 | image = dlib.load_rgb_image(image_loc) 334 | dets = detector(image, 1) 335 | 336 | faces = dlib.full_object_detections() 337 | for detection in dets: 338 | faces.append(shape_predictor(image, detection)) 339 | 340 | face_image = Image.fromarray(dlib.get_face_chip(image, faces[0], size=300)) 341 | 342 | return face_image 343 | 344 | 345 | def load_data(path_to_data): 346 | """ 347 | Helper function for loading image data. Allows user to load the input, target, 348 | and test images. Mask creation and offsetting must be done manually. 349 | 350 | Parameters 351 | ---------- 352 | path_to_data : str 353 | Path to the given data. Ex: './faces/input/' 354 | 355 | Returns 356 | ------- 357 | list : [PIL.Image] 358 | List of resized face images 359 | """ 360 | img_files = [f for f in os.listdir(path_to_data) if re.search(r'.*\.(jpe?g|png)', f)] 361 | img_files_locs = [os.path.join(path_to_data, f) for f in img_files] 362 | 363 | image_list = [] 364 | 365 | for loc in img_files_locs: 366 | image_list.append(detect_face(loc)) 367 | 368 | return image_list 369 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Attribution-ShareAlike 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. Distribution of 7 | Creative Commons public licenses does not create a lawyer-client or 8 | other relationship. 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Additional offer from the Licensor -- Adapted Material. 186 | Every recipient of Adapted Material from You 187 | automatically receives an offer from the Licensor to 188 | exercise the Licensed Rights in the Adapted Material 189 | under the conditions of the Adapter's License You apply. 190 | 191 | c. No downstream restrictions. You may not offer or impose 192 | any additional or different terms or conditions on, or 193 | apply any Effective Technological Measures to, the 194 | Licensed Material if doing so restricts exercise of the 195 | Licensed Rights by any recipient of the Licensed 196 | Material. 197 | 198 | 6. No endorsement. 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You may satisfy the conditions in Section 3(a)(1) in any 263 | reasonable manner based on the medium, means, and context in 264 | which You Share the Licensed Material. For example, it may be 265 | reasonable to satisfy the conditions by providing a URI or 266 | hyperlink to a resource that includes the required 267 | information. 268 | 269 | 3. If requested by the Licensor, You must remove any of the 270 | information required by Section 3(a)(1)(A) to the extent 271 | reasonably practicable. 272 | 273 | b. ShareAlike. 274 | 275 | In addition to the conditions in Section 3(a), if You Share 276 | Adapted Material You produce, the following conditions also apply. 277 | 278 | 1. The Adapter's License You apply must be a Creative Commons 279 | license with the same License Elements, this version or 280 | later, or a BY-SA Compatible License. 281 | 282 | 2. You must include the text of, or the URI or hyperlink to, the 283 | Adapter's License You apply. 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