4 citations · 7 across the 4 of their papers we have counts for
7 papers
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…
Deep learning in the ultrasound evaluation of neonatal respiratory status
Michela Gravina, Diego Gragnaniello, Luisa Verdoliva +9
Lung ultrasound imaging is reaching growing interest from the scientific community. On one side, thanks to its harmlessness and high descriptive power, this kind of diagnostic imag…
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…
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva +1
Due 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 prob…
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Diego Gragnaniello, Francesco Marra, Giovanni Poggi +1
Deep neural networks provide unprecedented performance in all image classification problems, taking advantage of huge amounts of data available for training. Recent studies, howeve…
Do GANs leave artificial fingerprints?
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva +1
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 pa…