Asja Fischer

Training-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples

This work proposes two novel attack methods targeted at detectors that leverage autoencoder reconstruction error and finds that by constructing imperceptible adversarial examples, the distance between original and reconstruction can be artificially increased, causing fake images to be wrongly classified as real.