Detecting Generative Parroting through Overfitting Masked Autoencoders
Saeid Asgari Taghanaki Joseph Lambourne
The advent of generative AI models has revolutionized digital content
creation, yet it introduces challenges in maintaining copyright integrity due
to generative parroting, where models mimic their training data too closely.
Our research presents a novel approach to tackle this issue by employing an
overfitted Masked Autoencoder (MAE) to detect such parroted samples
effectively. We establish a detection threshold based on the mean loss across
the training dataset, allowing for the precise identification of parroted
content in modified datasets. Preliminary evaluations demonstrate promising
results, suggesting our method's potential to ensure ethical use and enhance
the legal compliance of generative models.