# Adversarial Out-domain Examples for Generative Models

## TL;DR

This paper demonstrates how adversarial inputs can manipulate deep generative models, like GANs, to produce specific data and remain indistinguishable from genuine inputs, revealing security vulnerabilities.

## Contribution

It introduces a novel attack method that forces pre-trained generative models to reproduce targeted data, highlighting potential security risks in generative modeling.

## Key findings

- Adversarial latent vectors can produce arbitrary data instances.
- Generated adversarial inputs are statistically indistinguishable from genuine data.
- The attack is effective across various GAN architectures and training setups.

## Abstract

Deep generative models are rapidly becoming a common tool for researchers and developers. However, as exhaustively shown for the family of discriminative models, the test-time inference of deep neural networks cannot be fully controlled and erroneous behaviors can be induced by an attacker. In the present work, we show how a malicious user can force a pre-trained generator to reproduce arbitrary data instances by feeding it suitable adversarial inputs. Moreover, we show that these adversarial latent vectors can be shaped so as to be statistically indistinguishable from the set of genuine inputs. The proposed attack technique is evaluated with respect to various GAN images generators using different architectures, training processes and for both conditional and not-conditional setups.