# Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity

arXiv: https://arxiv.org/abs/2206.01230 · HTML: https://mlsanity.com/paper/2206.01230/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2022.

## Built on by (most cited first, 15 of 15)

- [Foundation Models and Fair Use](https://mlsanity.com/paper/2303.15715/) (2023)
- [On Provable Copyright Protection for Generative Models](https://mlsanity.com/paper/2302.10870/) (2023)
- [Can Copyright be Reduced to Privacy?](https://mlsanity.com/paper/2305.14822/) (2023)
- [CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images](https://mlsanity.com/paper/2310.16825/) (2023)
- [LiCoEval: Evaluating LLMs on License Compliance in Code Generation](https://mlsanity.com/paper/2408.02487/) (2024)
- [Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes](https://mlsanity.com/paper/2403.17691/) (2024)
- [Tackling Copyright Issues in AI Image Generation Through Originality Estimation and Genericization](https://mlsanity.com/paper/2406.03341/) (2024)
- [VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models](https://mlsanity.com/paper/2312.00057/) (2023)
- [Not Every Image is Worth a Thousand Words: Quantifying Originality in Stable Diffusion](https://mlsanity.com/paper/2408.08184/) (2024)
- [Credit Attribution and Stable Compression](https://mlsanity.com/paper/2406.15916/) (2024)
- [Randomization Techniques to Mitigate the Risk of Copyright Infringement](https://mlsanity.com/paper/2408.13278/) (2024)
- [Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture](https://mlsanity.com/paper/2410.04454/) (2024)
- [Interrogating LLM design under a fair learning doctrine](https://mlsanity.com/paper/2502.16290/) (2025)
- [Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models](https://mlsanity.com/paper/2506.19881/) (2025)
- [Reconstructing Template-Memorized Images from Natural Prompts](https://mlsanity.com/paper/2507.07947/) (2025)

## Abstract (arXiv)

A central notion in U.S. copyright law is judging the substantial similarity between an original and an (allegedly) derived work. Capturing this notion has proven elusive, and the many approaches offered by case law and legal scholarship are often ill-defined, contradictory, or internally-inconsistent. This work suggests that key parts of the substantial-similarity puzzle are amendable to modeling inspired by theoretical computer science. Our proposed framework quantitatively evaluates how much "novelty" is needed to produce the derived work with access to the original work, versus reproducing it without access to the copyrighted elements of the original work. "Novelty" is captured by a computational notion of description length, in the spirit of Kolmogorov-Levin complexity, which is robust to mechanical transformations and availability of contextual information. This results in an actionable framework that could be used by courts as an aid for deciding substantial similarity. We evaluate it on several pivotal cases in copyright law and observe that the results are consistent with the rulings, and are philosophically aligned with the abstraction-filtration-comparison test of Altai.