PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Models
On the map: LLM safety and security → Watermarking generated content and models
Abstract (arXiv)
Deep neural networks (DNNs) have achieved tremendous success in artificial intelligence (AI) fields. However, DNN models can be easily illegally copied, redistributed, or abused by criminals, seriously damaging the interests of model inventors. The copyright protection of DNN models by neural network watermarking has been studied, but the establishment of a traceability mechanism for determining the authorized users of a leaked model is a new problem driven by the demand for AI services. Because the existing traceability mechanisms are used for models without watermarks, a small number of false-positives are generated. Existing black-box active protection schemes have loose authorization control and are vulnerable to forgery attacks. Therefore, based on the idea of black-box neural network watermarking with the video framing and image perceptual hash algorithm, a passive copyright protection and traceability framework PCPT is proposed that uses an additional class of DNN models, improving the existing traceability mechanism that yields a small number of false-positives. Based on an authorization control strategy and image perceptual hash algorithm, a DNN model active copyright protection and traceability framework ACPT is proposed. This framework uses the authorization control center constructed by the detector and verifier. This approach realizes stricter authorization control, which establishes a strong connection between users and model owners, improves the framework security, and supports traceability verification.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security. Builds on 12 of its 46 references that are in the map. First posted on arXiv 6 Jun 2022.
Builds on
12 of 46 references.
Closest ancestors · 2
Same direction, newest first
- 2021Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images19 Apr
- 2021Deep Model Intellectual Property Protection via Deep Watermarking8 Mar
Other references · 10
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2014Going Deeper with Convolutionscited by 4,231
- 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 334
- 2017Embedding Watermarks into Deep Neural Networkscited by 227
- 2019[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attackscited by 82
- 2019How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNNcited by 58
- 2019Robust Watermarking of Neural Network with Exponential Weightingcited by 48
- 2018Have You Stolen My Model? Evasion Attacks Against Deep Neural Network Watermarking Techniquescited by 10
- 2021HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networkscited by 4
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Citing papers per year: 2023: 2.
2 papers.