Dr. Diego Gragnaniello
University of Naples Federico II, Department of Electrical Engineering and Information Technologies, via Claudio, 21, 80125 Napoli, Italy
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Academic Career Length
15Years (2012-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 March 2019
Conference Paper
FMFrancesco Marra
Francesco Marra
Diego GragnanielloLVLuisa Verdoliva
Luisa Verdoliva
GPGiovanni Poggi
Giovanni Poggi
In the last few years, generative adversarial networks (GAN) have shown tremendous potential for a number of applications in computer vision and related fields. With the current pace of progress, it is a sure bet they will soon be able to generate high-quality images and videos, virtually indistinguishable from real ones. Unfortunately, realistic GAN-generated images pose serious threats to security, to begin with a possible flood of fake multimedia, and multimedia forensic countermeasures are in urgent need. In this work, we show that each GAN leaves its specific fingerprint in the images it generates, just like real-world cameras mark acquired images with traces of their photo-response non-uniformity pattern. Source identification experiments with several popular GANs show such fingerprints to represent a precious asset for forensic analyses.
1 April 2018
Conference Paper
FMFrancesco Marra
Francesco MarraDiego Gragnaniello
Davide CozzolinoLVLuisa Verdoliva
Luisa Verdoliva
The diffusion of fake images and videos on social networks is a fast growing problem. Commercial media editing tools allow anyone to remove, add, or clone people and objects, to generate fake images. Many techniques have been proposed to detect such conventional fakes, but new attacks emerge by the day. Image-to-image translation, based on generative adversarial networks (GANs), appears as one of the most dangerous, as it allows one to modify context and semantics of images in a very realistic way. In this paper, we study the performance of several image forgery detectors against image-to-image translation, both in ideal conditions, and in the presence of compression, routinely performed upon uploading on social networks. The study, carried out on a dataset of 36302 images, shows that detection accuracies up to 95% can be achieved by both conventional and deep learning detectors, but only the latter keep providing a high accuracy, up to 89%, on compressed data.
5 July 2021
Conference Paper
Diego GragnanielloDCD. Cozzolino
D. Cozzolino
FMF. Marra
F. Marra
GPG. Poggi
G. Poggi
LVL. Verdoliva
L. Verdoliva
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 hardly distinguishable from real ones, raising serious concerns about the spread of fake or manipulated information over the Internet. In this context, it is important to develop automated tools to reliably and timely detect synthetic media. In this work, we analyze the state-of-the-art methods for the detection of synthetic images, highlighting the key ingredients of the most successful approaches, and comparing their performance over existing generative architectures. We will devote special attention to realistic and challenging scenarios, like media uploaded on social networks or generated by new and unseen architectures, analyzing the impact of suitable augmentation and training strategies on the detectors’ generalization ability.
16 February 2015 in IEEE Transactions on Information Forensics and Security
Journal Article
Diego GragnanielloGPGiovanni Poggi
Giovanni Poggi
Carlo SansoneLVLuisa Verdoliva
Luisa Verdoliva
Biometric authentication systems are quite vulnerable to sophisticated spoofing attacks. To keep a good level of security, reliable spoofing detection tools are necessary, preferably implemented as software modules. The research in this field is very active, with local descriptors, based on the analysis of microtextural features, gaining more and more popularity, because of their excellent performance and flexibility. This paper aims at assessing the potential of these descriptors for the liveness detection task in authentication systems based on various biometric traits: fingerprint, iris, and face. Besides compact descriptors based on the independent quantization of features, already considered for some liveness detection tasks, we will study promising descriptors based on the joint quantization of rich local features. The experimental analysis, conducted on publicly available data sets and in fully reproducible modality, confirms the potential of these tools for biometric applications, and points out possible lines of development toward further improvements.
16 July 2020 in IEEE Access
Journal Article
FMFrancesco Marra
Francesco MarraDiego GragnanielloLVLuisa Verdoliva
Luisa Verdoliva
Giovanni PoggiDue to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing. This is not a problem for high-level vision problems, where discriminative features are barely affected by resizing. On the contrary, in image forensics, resizing tends to destroy precious high-frequency details, impacting heavily on performance. One can avoid resizing by means of patch-wise processing, at the cost of renouncing whole-image analysis. In this work, we propose a CNN-based image forgery detection framework which makes decisions based on full-resolution information gathered from the whole image. Thanks to gradient checkpointing, the framework is trainable end-to-end with limited memory resources and weak (image-level) supervision, allowing for the joint optimization of all parameters. Experiments on widespread image forensics datasets prove the good performance of the proposed approach, which largely outperforms all baselines and all reference methods.
1 April 2015 in Pattern Recognition
Journal Article
Diego GragnanielloGPGiovanni Poggi
Giovanni Poggi
Carlo SansoneLVLuisa Verdoliva
Luisa Verdoliva
About Me
Diego Gragnaniello received a Ph.D. in electronic and telecommunications engineering in 2015 from the University of Naples Federico II, Italy. His thesis was awarded the Italian Computer Science Association prize for the year best thesis on Biometrics and Image Forensics. In 2017, he joined the National Research Council of Italy as a Researcher of the Cognitive system laboratory. He's currently affiliated with the Image Processing Research Group of the University of Naples Federico II.
He published more than 30 papers in international journals and main conferences. He participated in several research competitions in the image processing and pattern recognition field achieving top scores. He contributed to several research projects, among them the DARPA projects DISPARITY – Digital, Semantic and Physical Analysis of media integRITY (2016-2020) and SemaFor – Semantic Forensics (2020-2021).
He is serving as Guest Editor of the Special Issues on Advances in Digital Security: Biometrics and Forensics for Elsevier Pattern Recognition Letters. He served as Managing Guest Editor of the Special Issues on Adversarial Deep Learning in Biometrics & Forensics for Elsevier Computer Vision & Image Understanding, and the Special Issue on Advances in Deep Learning on MDPI Applied Science. He is Topic Editor for MDPI Applied Science and Forensic Sciences. He has been co-Chair of the 2nd International Workshop on Recent Advances in Digital Security: Biometrics and Forensics (BioFor), 2019.