DeepTracer: Tracing Stolen Model via Deep Coupled Watermarks

• URL: http://arxiv.org/abs/2511.08985v1
• Date: Thu, 13 Nov 2025 01:23:41 GMT
• Title: DeepTracer: Tracing Stolen Model via Deep Coupled Watermarks
• Authors: Yunfei Yang, Xiaojun Chen, Yuexin Xuan, Zhendong Zhao, Xin Zhao, He Li,
• Abstract summary: We introduce a robust watermarking framework, DeepTracer, which leverages a novel watermark samples construction method and a same-class coupling loss constraint.<n>DeepTracer can incur a high-coupling model between watermark task and primary task that makes adversaries learn the hidden watermark task when stealing the primary task functionality.<n>We propose an effective watermark samples filtering mechanism that elaborately select watermark key samples used in model ownership verification to enhance the reliability of watermarks.
• Score: 14.552367035706283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model watermarking techniques can embed watermark information into the protected model for ownership declaration by constructing specific input-output pairs. However, existing watermarks are easily removed when facing model stealing attacks, and make it difficult for model owners to effectively verify the copyright of stolen models. In this paper, we analyze the root cause of the failure of current watermarking methods under model stealing scenarios and then explore potential solutions. Specifically, we introduce a robust watermarking framework, DeepTracer, which leverages a novel watermark samples construction method and a same-class coupling loss constraint. DeepTracer can incur a high-coupling model between watermark task and primary task that makes adversaries inevitably learn the hidden watermark task when stealing the primary task functionality. Furthermore, we propose an effective watermark samples filtering mechanism that elaborately select watermark key samples used in model ownership verification to enhance the reliability of watermarks. Extensive experiments across multiple datasets and models demonstrate that our method surpasses existing approaches in defending against various model stealing attacks, as well as watermark attacks, and achieves new state-of-the-art effectiveness and robustness.

Related papers

• Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models [50.73220224678009]
  Watermarking can be used to verify the origin of synthetic images generated by artificial intelligence models.<n>Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial techniques.<n>In this paper, we uncover a greater risk of an optimization-free and universal watermark forgery.<n>Our approach significantly broadens the scope of attacks, presenting a greater challenge to the security of current watermarking techniques.
  arXiv  Detail & Related papers  (2025-06-06T12:08:02Z)
• SEAL: Semantic Aware Image Watermarking [26.606008778795193]
  We propose a novel watermarking method that embeds semantic information about the generated image directly into the watermark.<n>The key pattern can be inferred from the semantic embedding of the image using locality-sensitive hashing.<n>Our results suggest that content-aware watermarks can mitigate risks arising from image-generative models.
  arXiv  Detail & Related papers  (2025-03-15T15:29:05Z)
• RoboSignature: Robust Signature and Watermarking on Network Attacks [0.5461938536945723]
  We present a novel adversarial fine-tuning attack that disrupts the model's ability to embed the intended watermark.<n>Our findings emphasize the importance of anticipating and defending against potential vulnerabilities in generative systems.
  arXiv  Detail & Related papers  (2024-12-22T04:36:27Z)
• Trigger-Based Fragile Model Watermarking for Image Transformation Networks [2.38776871944507]
  In fragile watermarking, a sensitive watermark is embedded in an object in a manner such that the watermark breaks upon tampering. We introduce a novel, trigger-based fragile model watermarking system for image transformation/generation networks. Our approach, distinct from robust watermarking, effectively verifies the model's source and integrity across various datasets and attacks.
  arXiv  Detail & Related papers  (2024-09-28T19:34:55Z)
• ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training [50.77001916246691]
  This paper introduces ClearMark, the first DNN watermarking method designed for intuitive human assessment. ClearMark embeds visible watermarks, enabling human decision-making without rigid value thresholds. It shows an 8,544-bit watermark capacity comparable to the strongest existing work.
  arXiv  Detail & Related papers  (2023-10-25T08:16:55Z)
• Towards Robust Model Watermark via Reducing Parametric Vulnerability [57.66709830576457]
  backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model. We propose a mini-max formulation to find these watermark-removed models and recover their watermark behavior. Our method improves the robustness of the model watermarking against parametric changes and numerous watermark-removal attacks.
  arXiv  Detail & Related papers  (2023-09-09T12:46:08Z)
• Certified Neural Network Watermarks with Randomized Smoothing [64.86178395240469]
  We propose a certifiable watermarking method for deep learning models. We show that our watermark is guaranteed to be unremovable unless the model parameters are changed by more than a certain l2 threshold. Our watermark is also empirically more robust compared to previous watermarking methods.
  arXiv  Detail & Related papers  (2022-07-16T16:06:59Z)
• Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models [72.9364216776529]
  We propose a novel watermark removal attack from a different perspective. We design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations. Our attack can bypass state-of-the-art watermarking solutions with very high success rates.
  arXiv  Detail & Related papers  (2020-09-18T09:14:54Z)
• Model Watermarking for Image Processing Networks [120.918532981871]
  How to protect the intellectual property of deep models is a very important but seriously under-researched problem. We propose the first model watermarking framework for protecting image processing models.
  arXiv  Detail & Related papers  (2020-02-25T18:36:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.