activity
20172024
most citedRecasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

14 citations · 33 across the 8 of their papers we have counts for

collaborators

18 papers

cs.CV202210 cited

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…

cs.CV20221 cited

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…

cs.SD20223 cited

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…

cs.CV20212 cited

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…

cs.CV2020

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…

cs.CV20201 cited

Combining PRNU and noiseprint for robust and efficient device source identification

Davide Cozzolino, Francesco Marra, Diego Gragnaniello +2

PRNU-based image processing is a key asset in digital multimedia forensics. It allows for reliable device identification and effective detection and localization of image forgeries…