Founded in 2011, the Signal Processing and Communications Laboratory (LESC) brings together the Image Analysis, Processing, and Protection group with the Signal Processing and Wireless Communications group.

In this collaborative space, research activities encompass the analysis, processing and protection of multimedia signals such as images and video sequences), digital signal processing, wireless communications (WLAN, cellular, satellite), and telecommunications networks. Funding for the laboratory’s research comes from various projects supported by the Ministry of University and Research, the European Union, national and international research institutions (CNR, ESA, ASI, DARPA), and private companies.

Alongside their research, the IAPP and SPWC groups play a role in the educational activities of the School of Engineering. They offer courses for students in Computer Engineering, Electronic Engineering, Telecommunications Engineering, and Biomedical Engineering programs.

Latest news

Two papers accepted at WIFS 2024

2024-10-03

We are pleased to share that our latest contribution to image forensics research has led to the acceptance of two of our papers at the 16th IEEE International Workshop on Information Forensics and Security (WIFS/) 2024. 📚 Accepted Papers Tiny autoencoders are effective few-shot generative model detectors by Luca Bindini, Giulia Bertazzini, Daniele Baracchi, Dasara Shullani, Paolo Frasconi, and Alessandro Piva Beyond the Brush: Fully-automated Crafting of Realistic Inpainted Images by Giulia Bertazzini, Chiara Albisani Daniele Baracchi, Dasara Shullani, and Alessandro Piva Moreover, our first accepted paper is the outcome of a successful collaboration with Luca Bindini and Prof.

Identifying and Verifying Synthetic Media in the Age of AI Manipulation

2024-09-13

Dr. Daniele Baracchi and Dr. Dasara Shullani are happy to present the research topic Identifying and Verifying Synthetic Media in the Age of AI Manipulation. As topic editors, our aim is to bring together cutting-edge research that explores how to identify and verify AI-generated content. This includes finding ways to detect the subtle traces left by generative models and creating reliable tools for verifying the authenticity of multimedia content. Key dates to note:

Two papers accepted on Pattern Recognition Letters

2024-05-15

We’re happy to share that two of our papers have been accepted for publication in the Special Issue on Advances in Disinformation Detection and Media Forensics (A2DMF) in Pattern Recognition Letters. 📚 Accepted Papers Continual learning for adaptive social network identification by Simone Magistri, Daniele Baracchi, Dasara Shullani, Andrew D. Bagdanov, and Alessandro Piva Uncovering the authorship: Linking media content to social user profiles by Daniele Baracchi, Dasara Shullani, Massimo Iuliani, Damiano Giani, and Alessandro Piva The first accepted paper is the result of a fruitful collaboration with Simone Magistri and Prof.

International Conference on Acoustics, Speech and Signal Processing

2024-04-14

Dr. Daniele Baracchi and Dr. Dasara Shullani participated in the 49th International Conference on Acoustics, Speech, and Signal Processing in Seoul. They showcased their research during the poster session on Multimedia Forensics and Cybersecurity, as well as through an oral presentation in the Multimedia Forensics track. The conference featured a wealth of intriguing research ideas, with notable keynotes by Prof. Daniel D. Lee on Geometry and Latent Signal Representations in Machine Learning, and Prof.

Paper accepted on IEEE Access

2024-03-15

We are pleased to announce the publication of our latest paper in IEEE Access, a product of our strong collaboration with colleagues from the University of Trento as part of the UNCHAINED project. 📚 Accepted Paper Toward Open-World Multimedia Forensics Through Media Signature Encoding by Daniele Baracchi, Giulia Boato, Francesco de Natale, Massimo Iuliani, Andrea Montibeller, Cecilia Pasquini, Alessandro Piva, and Dasara Shullani About the authors Daniele Baracchi, Dasara Shullani, and Massimo Iuliani are postdoctoral researchers in the Department of Information Engineering at the University of Florence.

Machine Learning Summer School in Okinawa

2024-03-04

Chiara Albisani and Giulia Bertazzini participated in the Machine Learning Summer School at the Okinawa Institute of Science and Technology. Throughout the program, they delved into advanced machine learning techniques presented by leading industry experts. Highlights included remarkable lessons on Large Language Models by Prof. Tatsunori Hashimoto and Natural Language Processing by Prof. Diyi Yang. They also had the opportunity to showcase their research through a poster session, engaging in stimulating discussions with peers and experts.