# PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Models

arXiv: https://arxiv.org/abs/2206.02541 · HTML: https://mlsanity.com/paper/2206.02541/

## Key facts

- Cited by 2 papers in the map (data through 14 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.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks](https://mlsanity.com/paper/1909.07830/) (2019)
- [How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN](https://mlsanity.com/paper/1903.01743/) (2019)
- [Deep Model Intellectual Property Protection via Deep Watermarking](https://mlsanity.com/paper/2103.04980/) (2021)
- [Robust Watermarking of Neural Network with Exponential Weighting](https://mlsanity.com/paper/1901.06151/) (2019)
- [Have You Stolen My Model? Evasion Attacks Against Deep Neural Network Watermarking Techniques](https://mlsanity.com/paper/1809.00615/) (2018)
- [Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images](https://mlsanity.com/paper/2104.09203/) (2021)
- [HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks](https://mlsanity.com/paper/2103.13628/) (2021)

## Built on by (most cited first, 2 of 2)

- [Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond](https://mlsanity.com/paper/2306.00419/) (2023)
- [Turn Passive to Active: A Survey on Active Intellectual Property Protection of Deep Learning Models](https://mlsanity.com/paper/2310.09822/) (2023)

## 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.