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LixiaoTHULixiaoTHU
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‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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models/__pycache__/
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*.py[cod]
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*$py.class
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‎README.md

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# privacy_and_aug
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# Privacy, DA, and AT
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We take one DA method, Cutout, as an example:
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Train the 128 shadow models for Cutout:
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python train.py --train --s_model 0 --t_model 128 --aug_type cutout --dataset cifar10
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Inference $\phi$ for each data point with multiple queries (Ensure all models trained):
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python inference.py --mode eval --load_model --save_results --dataset cifar10 --query_mode multiple --aug_type cutout
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Perform LiRA after all $\phi$ computed (multiple queries):
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python eval_privacy.py --save_results --multi
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‎advtrain.py

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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils import data
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criterion_kl = nn.KLDivLoss(reduction='sum')
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def cal_adv(model, criterion, aug_type, imgs, cids, eps = 8):
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steps = 3
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step_size = 1.25 * eps / steps
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# steps = 20
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# step_size = 1
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model.eval()
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x_natural = imgs
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x_adv = x_natural.detach() + torch.empty_like(x_natural).uniform_(-eps / 255, eps / 255)
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if aug_type == "trades" or aug_type == "TradesAWP":
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logits = model.base_forward(x_natural)
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for _ in range(steps):
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x_adv.requires_grad_()
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with torch.enable_grad():
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loss_kl = criterion_kl(model(x_adv), F.softmax(logits, dim=1))
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grad = torch.autograd.grad(loss_kl, [x_adv])[0]
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x_adv = x_adv.detach() + (step_size / 255) * torch.sign(grad.detach())
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x_adv = torch.min(torch.max(x_adv, x_natural - eps / 255), x_natural + eps / 255)
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x_adv = torch.clamp(x_adv, 0.0, 1.0)
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return x_adv
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elif aug_type == "pgdat" or aug_type == "AWP":
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for _ in range(steps):
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x_adv.requires_grad_()
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with torch.enable_grad():
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pred = model(x_adv)
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loss = criterion(pred, cids)
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grad = torch.autograd.grad(loss, [x_adv])[0]
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x_adv = x_adv.detach() + (step_size / 255) * torch.sign(grad.detach())
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x_adv = torch.min(torch.max(x_adv, x_natural - eps / 255), x_natural + eps / 255)
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x_adv = torch.clamp(x_adv, 0.0, 1.0)
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return x_adv

‎configs/config_10.json

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{
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"note": "modified from https://github.com/yigitcankaya/augmentation_mia",
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"training_num_epochs": 100,
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"regular_batch_size": 256,
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"training_augmentations": ["none", "base", "smooth", "disturblabel", "noise", "cutout", "mixup", "jitter", "pgdat", "trades", "distillation", "AWP", "TradesAWP", "dandd"],
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"augmentation_params": {
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"distillation": [3, 1, 2, 5, 10],
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"smooth": [0.2, 0.05, 0.1, 0.01, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
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"disturblabel": [0.05, 0.01, 0.2, 0.1, 0.3, 0.4, 0.425, 0.45, 0.5, 0.525, 0.55, 0.575, 0.6],
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"noise": [0.01, 0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.275, 0.3, 0.325, 0.35],
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"cutout": [8, 4, 12, 16, 20],
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"mixup": [0.5, 0.1, 0.25, 1, 2, 4, 8, 16, 32, 64, 128, 256],
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"jitter": [0.05, 0.1, 0.2, 0.15, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5],
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"trades": [],
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"pgdat": [],
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"AWP": [],
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"TradesAWP": []
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}
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}

‎configs/config_100.json

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{
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"note": "modified from https://github.com/yigitcankaya/augmentation_mia",
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"training_num_epochs": 100,
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"regular_batch_size": 256,
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"training_augmentations": ["none", "base", "smooth", "disturblabel", "noise", "cutout", "mixup", "jitter", "pgdat", "trades", "distillation", "AWP", "TradesAWP", "dandd"],
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"augmentation_params": {
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"distillation": [3, 1, 2, 5, 10],
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"smooth": [0.3, 0.2, 0.05, 0.1, 0.01, 0.4, 0.5, 0.6, 0.7, 0.8],
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"disturblabel": [0.3, 0.05, 0.01, 0.2, 0.1, 0.4, 0.425, 0.45, 0.5, 0.525, 0.55, 0.575, 0.6],
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"noise": [0.01, 0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.275, 0.3, 0.325, 0.35],
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"cutout": [8, 4, 12, 16, 20],
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"mixup": [0.5, 0.1, 0.25, 1, 2, 4, 8, 16, 32, 64, 128, 256],
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"jitter": [0.05, 0.1, 0.2, 0.15, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5],
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"trades": [],
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"pgdat": [],
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"AWP": [],
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"TradesAWP": []
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}
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}

‎configs/locations.json

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{
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"note": "modified from https://github.com/yigitcankaya/augmentation_mia",
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"training_num_epochs": 50,
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"regular_batch_size": 256,
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"training_augmentations": ["none", "base", "smooth", "disturblabel", "noise", "distillation", "01flip"],
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"augmentation_params": {
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"distillation": [3, 1, 2, 5, 10],
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"smooth": [0.05, 0.01, 0.2, 0.1, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
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"disturblabel": [0.05, 0.01, 0.2, 0.1, 0.3, 0.4, 0.425, 0.45, 0.5, 0.525, 0.55, 0.575, 0.6],
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"noise": [0.35, 0.01, 0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.275, 0.3, 0.325],
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"trades": [],
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"pgdat": [],
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"AWP": [],
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"TradesAWP": [],
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"01flip": [0.1, 0.05, 0.2, 0.01, 0.3]
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}
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}

‎configs/purchase.json

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{
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"note": "modified from https://github.com/yigitcankaya/augmentation_mia",
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"training_num_epochs": 50,
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"regular_batch_size": 256,
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"training_augmentations": ["none", "base", "smooth", "disturblabel", "noise", "distillation", "01flip"],
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"augmentation_params": {
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"distillation": [3, 1, 2, 5, 10],
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"smooth": [0.05, 0.2, 0.1, 0.01, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
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"disturblabel": [0.2, 0.01, 0.05, 0.1, 0.3, 0.4, 0.425, 0.45, 0.5, 0.525, 0.55, 0.575, 0.6],
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"noise": [0.01, 0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.275, 0.3, 0.325, 0.35],
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"trades": [],
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"pgdat": [],
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"AWP": [],
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"TradesAWP": [],
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"01flip": [0.01, 0.05, 0.2, 0.1, 0.3]
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}
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}

‎configs/svhn.json

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{
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"note": "modified from https://github.com/yigitcankaya/augmentation_mia",
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"training_num_epochs": 100,
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"regular_batch_size": 256,
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"training_augmentations": ["none", "base", "smooth", "disturblabel", "noise", "cutout", "mixup", "jitter", "pgdat", "trades", "distillation", "AWP", "TradesAWP"],
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"augmentation_params": {
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"distillation": [3, 1, 2, 5, 10],
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"smooth": [0.2, 0.05, 0.1, 0.01, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
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"disturblabel": [0.05, 0.01, 0.2, 0.1, 0.3, 0.4, 0.425, 0.45, 0.5, 0.525, 0.55, 0.575, 0.6],
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"noise": [0.01, 0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.175, 0.2, 0.225, 0.25, 0.275, 0.3, 0.325, 0.35],
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"cutout": [8, 4, 12, 16, 20],
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"mixup": [0.5, 0.1, 0.25, 1, 2, 4, 8, 16, 32, 64, 128, 256],
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"jitter": [0.05, 0.1, 0.2, 0.15, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5],
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"trades": [],
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"pgdat": [],
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"AWP": [],
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"TradesAWP": []
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}
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}

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