Core Concepts
DNNShield introduces protection layers with unique identifiers to safeguard DNNs against unauthorized access and ownership disputes.
Abstract
The surge in popularity of machine learning has led to significant investments in training Deep Neural Networks (DNNs). However, these models are vulnerable to theft and unauthorized use. DNNShield addresses this challenge by embedding unique identifiers within the model architecture using specialized protection layers. These layers ensure secure training and deployment while offering high resilience against various attacks. The approach achieves security with minimal performance and computational overhead. Extensive evaluations across datasets and model architectures validate the effectiveness and efficiency of DNNShield in protecting DNNs and intellectual property rights.
I. Introduction
- ML advancements in various fields
- Importance of protecting DNNs
- Existing IP protection methods
II. Background
- Data representations in ML
- Neural network layers: Fully-Connected and Convolutional
- Metrics: Cosine similarity and accuracy
- DNN IP protection attacks: Fine-Tuning and Pruning
III. Requirement Analysis
- Motivation for developing a novel ownership verification method
- Considered scenario and threat model
- Objectives of the protection method
IV. DNNShield Design
- Integration of protection layers into model architecture
- Key generation and model training process
- Model inference and verification process
V. Security Analysis
- Attacks on protection layers: Merge, Convolutional Pattern Modification, Protection Layer Split
- Resilience of similarity metric (PAC)
VI. Evaluation
- Functionality of DNNShield with Hadamard and Permutation layers
- Position and amount of protection layers
- Model refinement and key replacement experiments
Stats
이 논문은 DNNShield가 모델 아키텍처에 고유 식별자를 포함하여 DNN을 보호하는 방법을 소개합니다.
DNNShield는 특수 보호층을 사용하여 모델 아키텍처에 고유 식별자를 포함하여 DNN을 불법 접근과 소유권 분쟁으로부터 보호합니다.
이 방법은 최소한의 성능 및 계산 오버헤드로 보안을 달성합니다.
DNNShield의 효과와 효율성을 검증하기 위해 다양한 데이터셋과 모델 아키텍처에서 광범위한 평가를 수행합니다.
Quotes
"DNNShield embeds unique identifiers within the model architecture using specialized protection layers."
"The approach achieves security with minimal performance and computational overhead."
"DNNShield adds novel protection layers into the architecture, that do not rely on secrecy and are untrainable."