384 citations · 1.2k across the 35 of their papers we have counts for
41 papers
Anchor-Regularized Adaptation for Generalizable AI-Generated Image Detection with DINOv3
Hyeongjun Choi, Juhun Lee, Davide Cozzolino +2
Recent works in AI-generated image detection have shown that careful training data alignment can improve generalization by removing spurious correlations. However, linear probes on…
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…
Chroma Clues: Leveraging Color Statistics to Detect Synthetic Images
Lea Uhlenbrock, Davide Cozzolino, Christian Riess
The evolution and dissemination of AI-synthesized images is occurring at an unprecedented rate. Image generators are making rapid progress in their goal of perfectly imitating natu…
Quality-Aware Calibration for AI-Generated Image Detection in the Wild
Fabrizio Guillaro, Vincenzo De Rosa, Davide Cozzolino +1
Significant progress has been made in detecting synthetic images, however most existing approaches operate on a single image instance and overlook a key characteristic of real-worl…
Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation
Riccardo Corvi, Davide Cozzolino, Ekta Prashnani +3
Synthetic video generation is progressing very rapidly. The latest models can produce very realistic high-resolution videos that are virtually indistinguishable from real ones. Alt…
AI-GenBench: A New Ongoing Benchmark for AI-Generated Image Detection
Lorenzo Pellegrini, Davide Cozzolino, Serafino Pandolfini +5
The rapid advancement of generative AI has revolutionized image creation, enabling high-quality synthesis from text prompts while raising critical challenges for media authenticity…
A Bias-Free Training Paradigm for More General AI-generated Image Detection
Fabrizio Guillaro, Giada Zingarini, Ben Usman +3
Successful forensic detectors can produce excellent results in supervised learning benchmarks but struggle to transfer to real-world applications. We believe this limitation is lar…
Zero-Shot Detection of AI-Generated Images
Davide Cozzolino, Giovanni Poggi, Matthias Nießner +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…
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…
Training-Free Deepfake Voice Recognition by Leveraging Large-Scale Pre-Trained Models
Alessandro Pianese, Davide Cozzolino, Giovanni Poggi +1
Generalization is a main issue for current audio deepfake detectors, which struggle to provide reliable results on out-of-distribution data. Given the speed at which more and more…
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…
Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition
Davide Cozzolino, Koki Nagano, Lucas Thomaz +2
The Video and Image Processing (VIP) Cup is a student competition that takes place each year at the IEEE International Conference on Image Processing. The 2022 IEEE VIP Cup asked u…
M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations
Giada Zingarini, Davide Cozzolino, Riccardo Corvi +2
The ability to detect manipulated visual content is becoming increasingly important in many application fields, given the rapid advances in image synthesis methods. Of particular c…
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
Riccardo Corvi, Davide Cozzolino, Giovanni Poggi +2
Detecting fake images is becoming a major goal of computer vision. This need is becoming more and more pressing with the continuous improvement of synthesis methods based on Genera…
TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization
Fabrizio Guillaro, Davide Cozzolino, Avneesh Sud +2
In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations bas…
On the detection of synthetic images generated by diffusion models
Riccardo Corvi, Davide Cozzolino, Giada Zingarini +3
Over the past decade, there has been tremendous progress in creating synthetic media, mainly thanks to the development of powerful methods based on generative adversarial networks…
Comprint: Image Forgery Detection and Localization using Compression Fingerprints
Hannes Mareen, Dante Vanden Bussche, Fabrizio Guillaro +4
Manipulation tools that realistically edit images are widely available, making it easy for anyone to create and spread misinformation. In an attempt to fight fake news, forgery det…
Deepfake audio detection by speaker verification
Alessandro Pianese, Davide Cozzolino, Giovanni Poggi +1
Thanks to recent advances in deep learning, sophisticated generation tools exist, nowadays, that produce extremely realistic synthetic speech. However, malicious uses of such tools…
Audio-Visual Person-of-Interest DeepFake Detection
Davide Cozzolino, Alessandro Pianese, Matthias Nießner +1
Face manipulation technology is advancing very rapidly, and new methods are being proposed day by day. The aim of this work is to propose a deepfake detector that can cope with the…
Towards Universal GAN Image Detection
Davide Cozzolino, Diego Gragnaniello, Giovanni Poggi +1
The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scena…
Forensic Analysis of Synthetically Generated Western Blot Images
Sara Mandelli, Davide Cozzolino, Edoardo D. Cannas +8
The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures. As a matter of fact, the generation of synthetic content is not r…
Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
Diego Gragnaniello, Davide Cozzolino, Francesco Marra +2
The advent of deep learning has brought a significant improvement in the quality of generated media. However, with the increased level of photorealism, synthetic media are becoming…
ID-Reveal: Identity-aware DeepFake Video Detection
Davide Cozzolino, Andreas Rössler, Justus Thies +2
A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor gen…