DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks

Abstract

This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend that is ubiquitous in various fields ranging from biomedical diagnosis to stock prediction. As the availability and popularity of pre-trained models are increasing, it is critical to protect the Intellectual Property (IP) of the model owner. DeepMarks introduces the first fingerprinting methodology that enables the model owner to embed unique fingerprints within the parameters (weights) of her model and later identify undesired usages of her distributed models. The proposed framework embeds the fingerprints in the Probability Density Function (pdf) of trainable weights by leveraging the extra capacity available in contemporary DL models. DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning. Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks (CNNs), corroborate the effectiveness and robustness of DeepMarks framework.

References in corpus (6)

• Deep Learning in Neural Networks: An Overview
• Caffe: Convolutional Architecture for Fast Feature Embedding
• MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
• Wide Residual Networks
• Embedding Watermarks into Deep Neural Networks
• Adversarial Frontier Stitching for Remote Neural Network Watermarking

Cited by in corpus (11)

• A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
• BlackMarks: Blackbox Multibit Watermarking for Deep Neural Networks
• Effectiveness of Distillation Attack and Countermeasure on Neural Network Watermarking
• Deep Neural Network Fingerprinting by Conferrable Adversarial Examples
• IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
• SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)
• Digital Passport: A Novel Technological Strategy for Intellectual Property Protection of Convolutional Neural Networks
• Training DNN Model with Secret Key for Model Protection
• [Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
• A Protection Method of Trained CNN Model Using Feature Maps Transformed With Secret Key From Unauthorized Access
• Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning