Search Results for author: Davide Cozzolino

Found 29 papers, 9 papers with code

Raising the Bar of AI-generated Image Detection with CLIP

no code implementations30 Nov 2023 Davide Cozzolino, Giovanni Poggi, Riccardo Corvi, Matthias Nießner, Luisa Verdoliva

Aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images.

Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition

no code implementations21 Sep 2023 Davide Cozzolino, Koki Nagano, Lucas Thomaz, Angshul Majumdar, Luisa Verdoliva

The Video and Image Processing (VIP) Cup is a student competition that takes place each year at the IEEE International Conference on Image Processing.

Synthetic Image Detection

TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization

no code implementations CVPR 2023 Fabrizio Guillaro, Davide Cozzolino, Avneesh Sud, Nicholas Dufour, Luisa Verdoliva

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 based on deep learning.

Image Forgery Detection Image Manipulation +1

On the detection of synthetic images generated by diffusion models

1 code implementation1 Nov 2022 Riccardo Corvi, Davide Cozzolino, Giada Zingarini, Giovanni Poggi, Koki Nagano, Luisa Verdoliva

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 (GAN).

Image Compression

Deepfake audio detection by speaker verification

no code implementations28 Sep 2022 Alessandro Pianese, Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva

Thanks to recent advances in deep learning, sophisticated generation tools exist, nowadays, that produce extremely realistic synthetic speech.

Face Swapping Speaker Verification +1

Audio-Visual Person-of-Interest DeepFake Detection

1 code implementation6 Apr 2022 Davide Cozzolino, Alessandro Pianese, Matthias Nießner, Luisa Verdoliva

The aim of this work is to propose a deepfake detector that can cope with the wide variety of manipulation methods and scenarios encountered in the real world.

Contrastive Learning DeepFake Detection +1

Towards Universal GAN Image Detection

no code implementations23 Dec 2021 Davide Cozzolino, Diego Gragnaniello, Giovanni Poggi, Luisa Verdoliva

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools.

Contrastive Learning

Forensic Analysis of Synthetically Generated Western Blot Images

no code implementations16 Dec 2021 Sara Mandelli, Davide Cozzolino, Edoardo D. Cannas, Joao P. Cardenuto, Daniel Moreira, Paolo Bestagini, Walter J. Scheirer, Anderson Rocha, Luisa Verdoliva, Stefano Tubaro, Edward J. Delp

As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images.

Binary Classification

Are GAN generated images easy to detect? A critical analysis of the state-of-the-art

1 code implementation6 Apr 2021 Diego Gragnaniello, Davide Cozzolino, Francesco Marra, Giovanni Poggi, Luisa Verdoliva

The advent of deep learning has brought a significant improvement in the quality of generated media.

ID-Reveal: Identity-aware DeepFake Video Detection

1 code implementation ICCV 2021 Davide Cozzolino, Andreas Rössler, Justus Thies, Matthias Nießner, Luisa Verdoliva

A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method.

Face Swapping Metric Learning

CNN-based fast source device identification

1 code implementation31 Jan 2020 Sara Mandelli, Davide Cozzolino, Paolo Bestagini, Luisa Verdoliva, Stefano Tubaro

Source identification is an important topic in image forensics, since it allows to trace back the origin of an image.

Image Forensics

SpoC: Spoofing Camera Fingerprints

no code implementations27 Nov 2019 Davide Cozzolino, Justus Thies, Andreas Rössler, Matthias Nießner, Luisa Verdoliva

Given a GAN-generated image, we insert the traces of a specific camera model into it and deceive state-of-the-art detectors into believing the image was acquired by that model.

Demosaicking Misinformation

FaceForensics++: Learning to Detect Manipulated Facial Images

14 code implementations25 Jan 2019 Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner

In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size.

DeepFake Detection Face Swapping +3

ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection

no code implementations6 Dec 2018 Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, Luisa Verdoliva

We devise a learning-based forensic detector which adapts well to new domains, i. e., novel manipulation methods and can handle scenarios where only a handful of fake examples are available during training.

Domain Adaptation

Guided patch-wise nonlocal SAR despeckling

1 code implementation28 Nov 2018 Sergio Vitale, Davide Cozzolino, Giuseppe Scarpa, Luisa Verdoliva, Giovanni Poggi

We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery.

Sar Image Despeckling Test

Noiseprint: a CNN-based camera model fingerprint

2 code implementations25 Aug 2018 Davide Cozzolino, Luisa Verdoliva

Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images.

Target-adaptive CNN-based pansharpening

no code implementations18 Sep 2017 Giuseppe Scarpa, Sergio Vitale, Davide Cozzolino

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art.

Pansharpening

Autoencoder with recurrent neural networks for video forgery detection

no code implementations29 Aug 2017 Dario D'Avino, Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva

Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos.

A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

no code implementations14 Mar 2017 Luca D'Amiano, Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva

We propose a new algorithm for the reliable detection and localization of video copy-move forgeries.

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

no code implementations14 Mar 2017 Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva

Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization.

Image Forgery Detection

A reliable order-statistics-based approximate nearest neighbor search algorithm

no code implementations11 Sep 2015 Luisa Verdoliva, Davide Cozzolino, Giovanni Poggi

We propose a new algorithm for fast approximate nearest neighbor search based on the properties of ordered vectors.

Image forgery detection based on the fusion of machine learning and block-matching methods

no code implementations27 Nov 2013 Davide Cozzolino, Diego Gragnaniello, Luisa Verdoliva

Dense local descriptors and machine learning have been used with success in several applications, like classification of textures, steganalysis, and forgery detection.

BIG-bench Machine Learning General Classification +2

A novel framework for image forgery localization

no code implementations27 Nov 2013 Davide Cozzolino, Diego Gragnaniello, Luisa Verdoliva

Image forgery localization is a very active and open research field for the difficulty to handle the large variety of manipulations a malicious user can perform by means of more and more sophisticated image editing tools.

BIG-bench Machine Learning Patch Matching

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