Adversarial Frontier Stitching for Remote Neural Network Watermarking (1711.01894v2)

Abstract: The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, [35, 22] proposed to watermark convolutional neural networks for image classification, by embedding information into their weights. While this is a clear progress towards model protection, this technique solely allows for extracting the watermark from a network that one accesses locally and entirely. Instead, we aim at allowing the extraction of the watermark from a neural network (or any other machine learning model) that is operated remotely, and available through a service API. To this end, we propose to mark the model's action itself, tweaking slightly its decision frontiers so that a set of specific queries convey the desired information. In the present paper, we formally introduce the problem and propose a novel zero-bit watermarking algorithm that makes use of adversarial model examples. While limiting the loss of performance of the protected model, this algorithm allows subsequent extraction of the watermark using only few queries. We experimented the approach on three neural networks designed for image classification, in the context of MNIST digit recognition task.

• The paper introduces a remote, zero-bit watermarking mechanism using adversarial frontier stitching for neural networks.
• It leverages adversarial perturbations to subtly modify decision boundaries while maintaining model performance.
• Empirical results on MNIST confirm the method's robustness and statistical reliability under adversarial conditions.

Adversarial Frontier Stitching for Remote Neural Network Watermarking

In the field of machine learning, neural networks are considered valuable intellectual properties due to their state-of-the-art performance. As these models become more entrenched in commercial applications, unauthorized access and model theft have emerged as critical concerns. The paper "Adversarial Frontier Stitching for Remote Neural Network Watermarking" by Le Merrer, Perez, and Tredan presents an innovative approach for securing neural network models in scenarios where they can be accessed only through an API. The authors propose a novel watermarking technique that facilitates watermark extraction remotely without direct access to the model's internal parameters.

Overview of Methodology

The paper introduces a zero-bit watermarking mechanism specifically designed for remotely accessible models. Unlike traditional techniques that require local access to model weights, this method imprints a watermark on the model's decision frontiers using adversarial examples. The watermarking process involves creating adversaries that slightly adjust the decision boundaries around selected inputs. This technique allows watermark verification through API interactions, thereby extending its applicability beyond local environments.

The core component of the proposed algorithm is the "frontier stitching" method, which employs adversarial perturbations to embed the watermark. By leveraging adversarial examples, the algorithm ensures that the integrity of the model's performance is maintained while embedding a watermark that is resilient to attempts at model manipulation. This is particularly critical, as the watermark must remain an unobtrusive component of the model's operational landscape to avoid detection and unauthorized removal.

Key Contributions and Findings

The contributions of the paper can be categorized as follows:

1. Formalization of Remote Watermarking: The paper provides a comprehensive framework and set of requirements for zero-bit watermarking of models operating in a black box setup.
2. Adversarial Frontier Stitching Algorithm: A practical approach that alters decision frontiers via adversaries to create a robust watermarking technique, ensuring minimal performance degradation.
3. Statistical Validation Framework: The use of a null hypothesis to assess the success of watermark extraction provides a robust statistical methodology for evaluating watermark presence.
4. Empirical Validation: Experimental results with neural networks on the MNIST dataset demonstrate the effectiveness and resilience of the proposed approach. These experiments confirm that the watermark can be reliably extracted remotely, even under adversarial conditions.

Implications and Future Directions

The implication of this research extends to enhancing model security for providers offering machine learning services through APIs. By fortifying model protections against illicit copying, businesses can maintain competitive advantages while sharing powerful models. The authors posit that future research could explore adaptations of the watermarking technique to regression problems and other neural architectures beyond image classification tasks.

Additionally, given the evolving landscape of adversarial attacks, future work can focus on the interplay between improving model robustness and preserving watermark integrity. Further, investigating methods for enhancing watermark resilience against transfer learning attacks is a promising research direction. The challenge remains to ensure that the watermark is both invisible to unauthorized entities and indelible amidst typical model adaptations.

In conclusion, the paper presents a forward-thinking approach to neural network watermarking, setting a foundation for future developments in model security. By harnessing adversarial examples strategically, this work paves the way for innovative solutions that honor the integrity and ownership of digital intellectual properties in machine learning.