InFIP: An Explainable DNN Intellectual Property Protection Method based on Intrinsic Features
On the map: LLM safety and security → Watermarking generated content and models
Abstract (arXiv)
Intellectual property (IP) protection for Deep Neural Networks (DNNs) has raised serious concerns in recent years. Most existing works embed watermarks in the DNN model for IP protection, which need to modify the model and lack of interpretability. In this paper, for the first time, we propose an interpretable intellectual property protection method for DNN based on explainable artificial intelligence. Compared with existing works, the proposed method does not modify the DNN model, and the decision of the ownership verification is interpretable. We extract the intrinsic features of the DNN model by using Deep Taylor Decomposition. Since the intrinsic feature is composed of unique interpretation of the model's decision, the intrinsic feature can be regarded as fingerprint of the model. If the fingerprint of a suspected model is the same as the original model, the suspected model is considered as a pirated model. Experimental results demonstrate that the fingerprints can be successfully used to verify the ownership of the model and the test accuracy of the model is not affected. Furthermore, the proposed method is robust to fine-tuning attack, pruning attack, watermark overwriting attack, and adaptive attack.
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 13 of its 26 references that are in the map. First posted on arXiv 14 Oct 2022.
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13 of 26 references.
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Other references · 12
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
- 2018Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooringcited by 334
- 2015Explaining NonLinear Classification Decisions with Deep Taylor Decompositioncited by 248
- 2017Embedding Watermarks into Deep Neural Networkscited by 227
- 2017Adversarial Frontier Stitching for Remote Neural Network Watermarkingcited by 110
- 2019IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundarycited by 68
- 2019REFIT: A Unified Watermark Removal Framework For Deep Learning Systems With Limited Datacited by 40
- 2020Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluationscited by 30
- 2020Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networkscited by 24
- 2022MetaV: A Meta-Verifier Approach to Task-Agnostic Model Fingerprintingcited by 16
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Citing papers per year: 2023: 2.
2 papers.