Navigating copyright infringement

Current models within AI detection

Text

Image

Video

Open AI classifier

Vision transformer model

Microsoft video authenticator

RoBERTa large OpenAI detector

GAN detector

Stable attribution (rendered) out of use by legal issues)

Giant language model test room

One example of a model uses a classifier method to give the text a label — “real” or “fake” — and a percentage score associated with it. An output is displayed below.

Fractal GenAI Text Dectector This tool uses the RoBERTa model to detect generative AI written text. It is best at detecting text using GPT-2

Additional Tools

Human evaluation The most proficient method for identifying attribution and copyright concerns within GenAI's output remains human assessment. A human reviewer can thoroughly examine the content and pinpoint any resemblances to copyrighted materials.

Machine learning approach Leveraging machine learning, we can formulate models designed to identify instances of copyright violation. By training these models on a data set comprising established copyrighted materials, they can subsequently be employed

Statistical examination Statistical analysis serves as an effective means to unveil text patterns indicative of copyright infringement. This methodology detects works likely to be derivative of others, even when no exact matches are present.

to scan novel works for potential infringement.

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