ExecuTorch模型水印：知识产权保护机制
项目地址: https://gitcode.com/GitHub_Trending/ex/executorch 引言：AI模型保护的迫切需求
在人工智能技术飞速发展的今天，深度学习模型已成为企业核心竞争力的重要组成部分。然而，模型盗用、非法复制和知识产权侵权问题日益严重。据行业统计，超过60%的AI企业曾遭遇模型泄露事件，造成平均数百万美元的经济损失。
ExecuTorch作为Meta推出的端到端设备端AI推理框架，为PyTorch模型提供了强大的部署解决方案。但在模型分发和部署过程中，如何有效保护模型知识产权成为亟待解决的问题。本文将深入探讨ExecuTorch框架中的模型水印技术，为开发者提供一套完整的知识产权保护方案。
ExecuTorch模型格式与保护基础
PTE文件格式解析
ExecuTorch使用专用的PTE（PyTorch ExecuTorch）文件格式存储优化后的模型。该格式基于FlatBuffers，具有以下结构特征：
模型验证机制
ExecuTorch提供了强大的IR（Intermediate Representation）验证机制，确保模型的完整性和正确性：
# EXIR验证器示例
from executorch.exir.verification import EXIREdgeDialectVerifier
# 创建验证器实例
verifier = EXIREdgeDialectVerifier()
# 验证模型完整性
def validate_model_integrity(graph_module):
try:
verifier(graph_module)
print("模型验证通过：完整性检查合格")
return True
except SpecViolationError as e:
print(f"模型验证失败：{e}")
return False
模型水印技术实现方案
静态水印：元数据嵌入
在PTE文件的ExtraTensorInfo中嵌入版权信息和水印数据：
import hashlib
import json
from datetime import datetime
def embed_watermark(tensor_info, owner_info, timestamp=None):
"""
在张量信息中嵌入数字水印
Args:
tensor_info: ExtraTensorInfo对象
owner_info: 所有者信息字典
timestamp: 时间戳（可选）
"""
if timestamp is None:
timestamp = datetime.now().isoformat()
# 创建水印数据
watermark_data = {
"owner": owner_info,
"timestamp": timestamp,
"signature": generate_signature(owner_info, timestamp)
}
# 将水印数据编码到fully_qualified_name字段
watermark_str = json.dumps(watermark_data, ensure_ascii=False)
tensor_info.fully_qualified_name = f"{tensor_info.fully_qualified_name}::watermark::{watermark_str}"
return tensor_info
def generate_signature(data, timestamp, secret_key="default_secret"):
"""生成数字签名"""
sign_data = f"{json.dumps(data)}{timestamp}{secret_key}"
return hashlib.sha256(sign_data.encode()).hexdigest()
动态水印：运行时验证
在模型推理过程中动态验证水印信息：
class ModelWatermarkVerifier:
def __init__(self, expected_owner_info, secret_key):
self.expected_owner_info = expected_owner_info
self.secret_key = secret_key
def extract_watermark(self, tensor_name):
"""从张量名称中提取水印信息"""
if "::watermark::" in tensor_name:
parts = tensor_name.split("::watermark::")
if len(parts) == 2:
try:
return json.loads(parts[1])
except json.JSONDecodeError:
return None
return None
def verify_watermark(self, program):
"""验证整个程序的水印完整性"""
violations = []
for execution_plan in program.execution_plan:
for value in execution_plan.values:
if hasattr(value.val, 'tensor') and value.val.tensor.extra_tensor_info:
tensor_info = value.val.tensor.extra_tensor_info
watermark = self.extract_watermark(tensor_info.fully_qualified_name)
if watermark:
if not self.validate_signature(watermark):
violations.append({
'tensor': tensor_info.fully_qualified_name,
'reason': '签名验证失败'
})
return len(violations) == 0, violations
def validate_signature(self, watermark):
"""验证数字签名"""
expected_signature = generate_signature(
watermark['owner'],
watermark['timestamp'],
self.secret_key
)
return watermark['signature'] == expected_signature
隐式水印：权重扰动技术
通过在模型权重中引入微小的、特定的扰动来嵌入水印：
import torch
import numpy as np
def embed_weight_watermark(model_state_dict, watermark_bits, strength=1e-6):
"""
在模型权重中嵌入隐式水印
Args:
model_state_dict: 模型状态字典
watermark_bits: 要嵌入的水印位（二进制数组）
strength: 扰动强度
"""
watermarked_state_dict = model_state_dict.copy()
bit_index = 0
for name, param in watermarked_state_dict.items():
if param.dim() >= 2: # 只在多维参数中嵌入水印
flat_param = param.view(-1)
for i in range(len(flat_param)):
if bit_index < len(watermark_bits):
# 根据水印位调整参数值
if watermark_bits[bit_index] == 1:
flat_param[i] += strength * torch.randn(1).item()
else:
flat_param[i] -= strength * torch.randn(1).item()
bit_index += 1
else:
break
if bit_index >= len(watermark_bits):
break
return watermarked_state_dict
def extract_weight_watermark(model_state_dict, original_state_dict, strength=1e-6):
"""
从模型权重中提取水印
"""
watermark_bits = []
for (name, param), (orig_name, orig_param) in zip(
model_state_dict.items(), original_state_dict.items()
):
if param.dim() >= 2 and orig_param.dim() >= 2:
flat_param = param.view(-1)
flat_orig = orig_param.view(-1)
for i in range(min(len(flat_param), len(flat_orig))):
diff = flat_param[i] - flat_orig[i]
# 根据差值判断水印位
if abs(diff) > strength / 2:
watermark_bits.append(1 if diff > 0 else 0)
return watermark_bits
多层次保护架构
保护层级设计
ExecuTorch模型水印保护采用多层次架构：
完整性验证机制
class ModelIntegrityGuard:
def __init__(self, expected_checksum, allowed_tolerance=1e-8):
self.expected_checksum = expected_checksum
self.allowed_tolerance = allowed_tolerance
def compute_model_checksum(self, program):
"""计算模型的校验和"""
checksum_data = []
# 收集所有常量数据
for buffer in program.constant_buffer:
checksum_data.append(buffer.storage.tobytes())
# 收集所有执行计划信息
for plan in program.execution_plan:
checksum_data.append(plan.name.encode())
for op in plan.operators:
checksum_data.append(op.name.encode())
checksum_data.append(op.overload.encode())
# 计算SHA-256校验和
combined_data = b''.join(checksum_data)
return hashlib.sha256(combined_data).hexdigest()
def verify_integrity(self, program):
"""验证模型完整性"""
current_checksum = self.compute_model_checksum(program)
return current_checksum == self.expected_checksum
def monitor_runtime_behavior(self, inputs, outputs):
"""监控运行时行为异常"""
# 实现异常检测逻辑
pass
实战：端到端保护方案
模型导出时的水印嵌入
from executorch import export
from executorch.exir import ExecutorchProgramManager
import torch.nn as nn
class ProtectedModelExporter:
def __init__(self, owner_info, secret_key):
self.owner_info = owner_info
self.secret_key = secret_key
self.watermark_verifier = ModelWatermarkVerifier(owner_info, secret_key)
def export_with_protection(self, model, example_inputs, output_path):
"""导出带保护的模型"""
# 1. 标准导出流程
export_session = export.export(
model=model,
example_inputs=example_inputs,
export_recipe=export.ExportRecipe()
)
# 2. 获取Executorch程序
program = export_session.get_executorch_program()
# 3. 嵌入水印
self.embed_all_watermarks(program)
# 4. 计算完整性校验和
integrity_guard = ModelIntegrityGuard()
checksum = integrity_guard.compute_model_checksum(program)
# 5. 保存模型
export_session.save_pte_file(output_path)
return checksum
def embed_all_watermarks(self, program):
"""在所有张量中嵌入水印"""
for execution_plan in program.execution_plan:
for i, value in enumerate(execution_plan.values):
if hasattr(value.val, 'tensor') and value.val.tensor.extra_tensor_info:
tensor_info = value.val.tensor.extra_tensor_info
watermarked_info = embed_watermark(
tensor_info,
self.owner_info,
datetime.now().isoformat()
)
value.val.tensor.extra_tensor_info = watermarked_info
运行时保护集成
class ProtectedModelRunner:
def __init__(self, model_path, expected_checksum, owner_info, secret_key):
self.model_path = model_path
self.expected_checksum = expected_checksum
self.owner_info = owner_info
self.secret_key = secret_key
# 加载模型
self.program = self.load_model()
# 初始化验证器
self.integrity_guard = ModelIntegrityGuard(expected_checksum)
self.watermark_verifier = ModelWatermarkVerifier(owner_info, secret_key)
def load_model(self):
"""加载并验证模型"""
# 实现模型加载逻辑
pass
def run_with_protection(self, inputs):
"""带保护的模型推理"""
# 1. 验证模型完整性
if not self.integrity_guard.verify_integrity(self.program):
raise SecurityError("模型完整性验证失败")
# 2. 验证水印
is_valid, violations = self.watermark_verifier.verify_watermark(self.program)
if not is_valid:
raise SecurityError(f"水印验证失败：{violations}")
# 3. 执行推理
outputs = self.execute_model(inputs)
# 4. 监控运行时行为
self.monitor_runtime_behavior(inputs, outputs)
return outputs
def execute_model(self, inputs):
"""执行模型推理"""
# 实现推理逻辑
pass
def monitor_runtime_behavior(self, inputs, outputs):
"""监控运行时异常行为"""
# 实现行为监控逻辑
pass
高级保护特性
时间戳与版本控制
class VersionAwareWatermark:
def __init__(self):
self.version_registry = {}
def register_version(self, model_id, version_info, watermark_data):
"""注册模型版本信息"""
self.version_registry[model_id] = {
'version_info': version_info,
'watermark_data': watermark_data,
'timestamp': datetime.now().isoformat()
}
def verify_version_compatibility(self, program, expected_model_id):
"""验证模型版本兼容性"""
extracted_watermarks = self.extract_all_watermarks(program)
if expected_model_id not in self.version_registry:
return False, "模型ID未注册"
registered_data = self.version_registry[expected_model_id]
# 检查水印一致性
for watermark in extracted_watermarks:
if watermark.get('model_id') == expected_model_id:
if watermark.get('version_hash') != registered_data['version_info']['hash']:
return False, "版本哈希不匹配"
return True, "版本验证通过"
def extract_all_watermarks(self, program):
"""提取所有水印信息"""
watermarks = []
# 实现提取逻辑
return watermarks
动态水印更新机制
class DynamicWatermarkManager:
def __init__(self, update_interval=3600): # 默认1小时更新一次
self.update_interval = update_interval
self.last_update = {}
def should_update_watermark(self, model_id):
"""检查是否需要更新水印"""
current_time = time.time()
last_update = self.last_update.get(model_id, 0)
return current_time - last_update >= self.update_interval
def update_dynamic_watermark(self, program, model_id):
"""更新动态水印"""
if self.should_update_watermark(model_id):
# 生成新的动态水印
dynamic_watermark = self.generate_dynamic_watermark(model_id)
# 更新程序中的水印
self.update_program_watermarks(program, dynamic_watermark)
self.last_update[model_id] = time.time()
return True
return False
def generate_dynamic_watermark(self, model_id):
"""生成动态水印数据"""
return {
'model_id': model_id,
'timestamp': datetime.now().isoformat(),
'nonce': os.urandom(16).hex(),
'signature': self.generate_dynamic_signature(model_id)
}
安全最佳实践
密钥管理策略
class SecureKeyManager:
def __init__(self, key_storage_path=None):
self.key_storage_path = key_storage_path
self.keys = self.load_keys()
def load_keys(self):
"""安全加载密钥"""
keys = {}
if self.key_storage_path and os.path.exists(self.key_storage_path):
try:
with open(self.key_storage_path, 'rb') as f:
encrypted_data = f.read()
decrypted_data = self.decrypt_data(encrypted_data)
keys = json.loads(decrypted_data)
except Exception as e:
print(f"密钥加载失败：{e}")
keys = self.generate_new_keys()
else:
keys = self.generate_new_keys()
return keys
def generate_new_keys(self):
"""生成新密钥对"""
return {
'watermark_secret': secrets.token_hex(32),
'integrity_secret': secrets.token_hex(32),
'version_secret': secrets.token_hex(32)
}
def get_key(self, key_type):
"""获取特定类型的密钥"""
return self.keys.get(key_type)
def rotate_keys(self):
"""轮换密钥"""
new_keys = self.generate_new_keys()
self.keys.update(new_keys)
self.save_keys()
def save_keys(self):
"""安全保存密钥"""
if self.key_storage_path:
encrypted_data = self.encrypt_data(json.dumps(self.keys).encode())
with open(self.key_storage_path, 'wb') as f:
f.write(encrypted_data)
审计与日志记录
class ProtectionAuditLogger:
def __init__(self, log_path=None):
self.log_path = log_path
self.audit_entries = []
def log_verification_event(self, event_type, success, details=None):
"""记录验证事件"""
audit_entry = {
'timestamp': datetime.now().isoformat(),
'event_type': event_type,
'success': success,
'details': details or {},
'client_ip': self.get_client_ip(),
'user_agent': self.get_user_agent()
}
self.audit_entries.append(audit_entry)
# 实时写入日志文件
if self.log_path:
self.write_to_log(audit_entry)
def write_to_log(self, entry):
"""写入日志文件"""
log_line = json.dumps(entry) + '\n'
with open(self.log_path, 'a', encoding='utf-8') as f:
f.write(log_line)
def get_security_report(self):
"""生成安全报告"""
total_events = len(self.audit_entries)
success_events = sum(1 for e in self.audit_entries if e['success'])
failure_events = total_events - success_events
return {
'total_events': total_events,
'success_rate': success_events / total_events if total_events > 0 else 0,
'recent_failures': self.get_recent_failures(24), # 最近24小时的失败
'common_failure_types': self.get_common_failure_types()
}
性能优化考虑
轻量级验证策略
class OptimizedWatermarkVerifier:
def __init__(self, sampling_rate=0.1): # 10%的采样率
self.sampling_rate = sampling_rate
def optimized_verify(self, program):
"""优化版水印验证（抽样检查）"""
import random
# 收集所有包含水印的张量
watermarked_tensors = []
for execution_plan in program.execution_plan:
for value in execution_plan.values:
if (hasattr(value.val, 'tensor') and
value.val.tensor.extra_tensor_info and
"::watermark::" in value.val.tensor.extra_tensor_info.fully_qualified_name):
watermarked_tensors.append(value)
# 随机抽样验证
sample_size = max(1, int(len(watermarked_tensors) * self.sampling_rate))
sampled_tensors = random.sample(watermarked_tensors, sample_size)
violations = []
for tensor_value in sampled_tensors:
tensor_info = tensor_value.val.tensor.extra_tensor_info
watermark = self.extract_watermark(tensor_info.fully_qualified_name)
if watermark and not self.validate_signature(watermark):
violations.append({
'tensor': tensor_info.fully_qualified_name,
'reason': '签名验证失败'
})
return len(violations) == 0, violations
缓存优化机制
创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考
