File size: 6,041 Bytes
a4d55b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | from __future__ import annotations
import json
import random
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import trackio
from model import RobustTinyCNN, parameter_count
from safetensors.torch import load_file, save_file
from sklearn.metrics import accuracy_score
from torch.nn import functional as F
from torch.utils.data import DataLoader, TensorDataset
PROJECT_DIR = Path(__file__).resolve().parent
ROOT_DIR = PROJECT_DIR.parents[1]
SOURCE_DIR = ROOT_DIR / "projects" / "tiny-vision-foundry"
DATA_DIR = SOURCE_DIR / "data"
BASE_WEIGHTS = SOURCE_DIR / "artifacts" / "tiny-student-scratch" / "model.safetensors"
ARTIFACT_DIR = PROJECT_DIR / "artifacts" / "pixel-shield-robust"
def seed_everything(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
def load_split(name: str, *, shuffle: bool, batch_size: int) -> DataLoader:
frame = pd.read_parquet(DATA_DIR / f"{name}.parquet")
pixels = np.stack(frame["image"].to_numpy()).astype(np.float32) / 16.0
images = torch.from_numpy(pixels.reshape(-1, 1, 8, 8))
labels = torch.from_numpy(frame["label"].to_numpy(dtype=np.int64, copy=True))
return DataLoader(
TensorDataset(images, labels),
batch_size=batch_size,
shuffle=shuffle,
generator=torch.Generator().manual_seed(2026),
)
def fgsm(
model: RobustTinyCNN,
pixels: torch.Tensor,
labels: torch.Tensor,
epsilon: float,
) -> torch.Tensor:
attacked = pixels.detach().clone().requires_grad_(True)
loss = F.cross_entropy(model(attacked), labels)
gradient = torch.autograd.grad(loss, attacked)[0]
return torch.clamp(attacked + epsilon * gradient.sign(), 0, 1).detach()
def evaluate(model: RobustTinyCNN, loader: DataLoader, epsilon: float) -> float:
model.eval()
labels, predictions = [], []
for pixels, targets in loader:
if epsilon:
pixels = fgsm(model, pixels, targets, epsilon)
with torch.no_grad():
logits = model(pixels)
labels.extend(targets.tolist())
predictions.extend(logits.argmax(dim=1).tolist())
return float(accuracy_score(labels, predictions))
def benchmark(model: RobustTinyCNN, loader: DataLoader) -> dict[str, float]:
return {
f"epsilon_{epsilon:.2f}": evaluate(model, loader, epsilon)
for epsilon in [0.0, 0.05, 0.10, 0.15, 0.20, 0.25]
}
def main() -> None:
seed_everything(2030)
if not BASE_WEIGHTS.exists():
raise FileNotFoundError("Train Tiny Vision Foundry before running PixelShield.")
train_loader = load_split("train", shuffle=True, batch_size=64)
validation_loader = load_split("validation", shuffle=False, batch_size=256)
test_loader = load_split("test", shuffle=False, batch_size=256)
baseline = RobustTinyCNN()
baseline.load_state_dict(load_file(BASE_WEIGHTS))
baseline_metrics = benchmark(baseline, test_loader)
robust = RobustTinyCNN()
robust.load_state_dict(load_file(BASE_WEIGHTS))
optimizer = torch.optim.AdamW(robust.parameters(), lr=0.0015, weight_decay=0.002)
epochs = 50
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
best_score = -1.0
best_epoch = 0
best_state = None
trackio.init(
project="pixel-shield",
name="tiny-student-fgsm-training-v1",
config={
"parameters": parameter_count(robust),
"epochs": epochs,
"training_epsilon": 0.15,
"clean_adversarial_mix": "50/50",
},
)
for epoch in range(1, epochs + 1):
robust.train()
running_loss = 0.0
examples = 0
for pixels, labels in train_loader:
adversarial = fgsm(robust, pixels, labels, epsilon=0.15)
combined_pixels = torch.cat([pixels, adversarial])
combined_labels = torch.cat([labels, labels])
logits = robust(combined_pixels)
loss = F.cross_entropy(logits, combined_labels)
optimizer.zero_grad(set_to_none=True)
loss.backward()
optimizer.step()
running_loss += loss.item() * len(combined_labels)
examples += len(combined_labels)
scheduler.step()
clean_accuracy = evaluate(robust, validation_loader, epsilon=0.0)
robust_accuracy = evaluate(robust, validation_loader, epsilon=0.15)
selection_score = 0.35 * clean_accuracy + 0.65 * robust_accuracy
trackio.log(
{
"epoch": epoch,
"train_loss": running_loss / examples,
"validation_clean_accuracy": clean_accuracy,
"validation_fgsm_0.15_accuracy": robust_accuracy,
"selection_score": selection_score,
"learning_rate": scheduler.get_last_lr()[0],
}
)
if selection_score > best_score:
best_score = selection_score
best_epoch = epoch
best_state = {
key: value.detach().cpu().clone()
for key, value in robust.state_dict().items()
}
trackio.finish()
assert best_state is not None
robust.load_state_dict(best_state)
robust_metrics = benchmark(robust, test_loader)
results = {
"model": "PixelShield Robust Tiny CNN",
"parameters": parameter_count(robust),
"best_epoch": best_epoch,
"attack": "white-box FGSM over normalized [0,1] pixels",
"baseline": baseline_metrics,
"adversarially_trained": robust_metrics,
"accuracy_delta": {
key: robust_metrics[key] - baseline_metrics[key] for key in baseline_metrics
},
}
ARTIFACT_DIR.mkdir(parents=True, exist_ok=True)
save_file(robust.state_dict(), ARTIFACT_DIR / "model.safetensors")
(ARTIFACT_DIR / "evaluation.json").write_text(
json.dumps(results, indent=2),
encoding="utf-8",
)
print(json.dumps(results, indent=2))
if __name__ == "__main__":
main()
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