MGI: Member vs Generated Inference

The article introduces the concept of Member vs Generated Inference (MGI), which addresses the challenge of distinguishing between samples from a generative model's training set and samples generated by the model itself. It presents a novel method called Data Circuit Breaker (DCB), which utilizes a three-stage approach combining signals from an autoencoder and latent generator, demonstrating effectiveness across various generative models, including image autoregressive and diffusion models. This advancement is significant for practitioners as it enhances the reliability of membership inference in scenarios where models may reproduce training data, thereby improving the security and integrity of generative AI applications.