P
Paolo Bestagini
Researcher at Polytechnic University of Milan
Publications - 150
Citations - 3193
Paolo Bestagini is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 23, co-authored 118 publications receiving 2093 citations. Previous affiliations of Paolo Bestagini include Instituto Politécnico Nacional & Purdue University.
Papers
More filters
Journal ArticleDOI
An overview on video forensics
Paolo Bestagini,K. M. Fontani,Simone Milani,Mauro Barni,Alessandro Piva,Marco Tagliasacchi,K. S. Tubaro +6 more
TL;DR: The paper aims at providing an overview of the existing video processing techniques, considering all the possible alterations that can be operated on a single signal and also the possibility of identifying the traces that could reveal important information about its origin and use.
Journal ArticleDOI
First Steps Toward Camera Model Identification with Convolutional Neural Networks
TL;DR: Zhang et al. as discussed by the authors proposed a data-driven algorithm based on convolutional neural networks, which learns features characterizing each camera model directly from the acquired pictures, and showed that the proposed method outperforms up-to-date state-of-the-art algorithms on classification of 64 × 64 color image patches.
Journal ArticleDOI
Aligned and non-aligned double JPEG detection using convolutional neural networks
Mauro Barni,Luca Bondi,Nicolo Bonettini,Paolo Bestagini,Andrea Costanzo,Marco Maggini,Benedetta Tondi,Stefano Tubaro +7 more
TL;DR: This paper explores the capability of CNNs to capture DJPEG artifacts directly from images and shows that the proposed CNN-based detectors achieve good performance even with small size images, outperforming state-of-the-art solutions, especially in the non-aligned case.
Proceedings ArticleDOI
Tampering Detection and Localization Through Clustering of Camera-Based CNN Features
TL;DR: An algorithm for image tampering detection and localization, leveraging characteristic footprints left on images by different camera models to detect whether an image has been forged, and localize the alien region.
Proceedings ArticleDOI
Local tampering detection in video sequences
TL;DR: The analysis of the footprints left when tampering with a video sequence is presented, and a detection algorithm is proposed that allows a forensic analyst to reveal video forgeries and localize them in the spatio-temporal domain.