Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring

Abstract: Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expensive and requires vast amounts of training data. Selling such pre-trained models can, therefore, be a lucrative business model. Unfortunately, once the models are sold they can be easily copied and redistributed. To avoid this, a tracking mechanism to identify models as the intellectual property of a particular vendor is necessary. In this work, we present an approach for watermarking Deep Neural Networks in a black-box way. Our scheme works for general classification tasks and can easily be combined with current learning algorithms. We show experimentally that such a watermark has no noticeable impact on the primary task that the model is designed for and evaluate the robustness of our proposal against a multitude of practical attacks. Moreover, we provide a theoretical analysis, relating our approach to previous work on backdooring.

• The paper introduces a novel backdoor watermarking technique that embeds unique signatures in deep neural networks.
• The paper provides theoretical analysis and experimental validation on benchmarks like CIFAR-10, CIFAR-100, and ImageNet.
• The method preserves core model functionality while ensuring watermark robustness against retraining and fine-tuning attacks.

Watermarking Deep Neural Networks by Backdooring

The paper "Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring" presents an innovative approach to protect the intellectual property of deep neural networks (DNNs). This research introduces a method to watermark DNNs by exploiting a technique known as backdooring, providing an essential solution to the challenge of unauthorized duplication and distribution of pre-trained models.

Overview

Deep neural networks have become indispensable across various domains, providing breakthroughs in tasks ranging from image recognition to natural language processing. The development of these models involves substantial computational resources and expertise. Consequently, pre-trained DNNs represent significant value and are often distributed by companies under licensing agreements. However, the ease with which these models can be copied poses a security risk, as unauthorized redistribution can undercut the original creators' revenue. Therefore, a robust, non-intrusive method to watermark DNNs is required to assert ownership and detect intellectual property theft.

Methodology

The authors propose a method for embedding watermarks into neural networks using backdooring. Backdooring, typically seen as a vulnerability, allows the modification of model responses for specific inputs. This property is harnessed to encode a watermark by setting deliberate misclassifications for a fixed set of inputs known as the trigger set. The main contributions of their approach include:

1. Robust Watermarking:
   • The technique involves modifying the learning process to embed a watermark without compromising the model's primary function. The watermarking is achieved through backdoor mechanisms where a subset of inputs results in predetermined incorrect outputs, serving as a signature of ownership.
2. Theoretical and Practical Analysis:
   • The paper provides a theoretical analysis demonstrating the relationship between backdooring and digital watermarking. It establishes how properties of strong backdoors, including persistence and hidden trigger sets, contribute to effective watermarking.
3. Experimental Validation:
   • Extensive experiments were conducted across well-recognized benchmarks such as CIFAR-10, CIFAR-100, and ImageNet. These experiments validate the watermark's robustness against re-training and fine-tuning attacks, which are common scenarios where an attacker might attempt to remove or alter the watermark.

Implications

The presented method has significant implications for the protection and licensing of DNNs:

• Practical Implications:
  • The robustness of the watermarking method ensures that companies can track and assert ownership of their models even when they are modified or fine-tuned by end-users. This secures both the economic value and fidelity of pre-trained models in a market heavily reliant on trust and integrity.
• Theoretical Insights:
  • The formal relationship between watermarking and backdooring expands the understanding of neural network vulnerabilities and security applications. It provides a novel perspective on how over-parameterization in neural networks can be repurposed from a security weakness into a valuable property for defending intellectual property.

Future Directions

Given the promising results and potential applications, future directions could explore enhancing the scalability of watermarking techniques. Embedding watermarks that can withstand transfer learning across significantly different datasets or architectures remains a challenge. Additionally, developing public verification protocols that maximize transparency while safeguarding proprietary information would bolster the practical deployment of this technology in real-world systems.

In conclusion, this paper presents a detailed exploration of leveraging backdoor mechanisms for watermarking neural networks. It makes a convincing case for converting a traditionally perceived weakness into a protective mechanism, offering both theoretical advancements and practical solutions to address pressing concerns in the commercialization of DNNs.