5 papers · 1 filter
Understanding Why Foundation Models Work for Diffusion-Generated Image Detection
Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva
Vision foundation models have recently emerged as powerful feature extractors for detecting AI-generated images, achieving strong generalization across generators and robustness to…
Exploring the Adversarial Robustness of CLIP for AI-generated Image Detection
Vincenzo De Rosa, Fabrizio Guillaro, Giovanni Poggi +2
In recent years, many forensic detectors have been proposed to detect AI-generated images and prevent their use for malicious purposes. Convolutional neural networks (CNNs) have lo…
Zero-Shot Detection of AI-Generated Images
Davide Cozzolino, Giovanni Poggi, Matthias NieÃner +1
Detecting AI-generated images has become an extraordinarily difficult challenge as new generative architectures emerge on a daily basis with more and more capabilities and unpreced…
Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yet
Diangarti Tariang, Riccardo Corvi, Davide Cozzolino +3
In this work we present an overview of approaches for the detection and attribution of synthetic images and highlight their strengths and weaknesses. We also point out and discuss…
Raising the Bar of AI-generated Image Detection with CLIP
Davide Cozzolino, Giovanni Poggi, Riccardo Corvi +2
The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection str…