Francesco Marra
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Information Systems top 10%
- Media Technology top 10%
- Co-authors
- Luisa VerdolivaDiego GragnanielloGiovanni PoggiDavide CozzolinoCarlo SansoneCristiano SaltoriGiulia BoatoFabio Roli
- Topics
- Digital Media Forensic Detection (11 papers)Advanced Steganography and Watermarking Techniques (4 papers)Generative Adversarial Networks and Image Synthesis (4 papers)
- Journals
- Pattern Recognition LettersIEEE Transactions on Information Forensics and SecurityMultimedia Tools and Applications
- Partner nations
- Italy
In The Last Decade
Francesco Marra
12 papers receiving 684 citations
Peers
Comparison fields: 5 of 54
- Computer Vision and Pattern Recognition 623
- Artificial Intelligence 189
- Signal Processing 77
- Information Systems 51
- Media Technology 48
Countries citing papers authored by Francesco Marra
This map shows the geographic impact of Francesco Marra's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Francesco Marra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Marra more than expected).
Fields of papers citing papers by Francesco Marra
This network shows the impact of papers produced by Francesco Marra. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Francesco Marra. The network helps show where Francesco Marra may publish in the future.
Co-authorship network of co-authors of Francesco Marra
This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Marra. A scholar is included among the top collaborators of Francesco Marra based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Francesco Marra. Francesco Marra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 80 | |
| 2 | 184 | |
| 3 | 225 | |
| 4 | 19 | |
| 5 | 29 | |
| 6 | 60 | |
| 7 | 45 | |
| 8 | 32 | |
| 9 | 16 | |
| 10 | 15 | |
| 11 | 6 | |
| 12 | Multi-Physics Modeling as a Design Tool: Advances and Prospects | 2 |
About Francesco Marra
Francesco Marra is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Law, having authored 12 papers that have together received 713 indexed citations. Recurring topics across this work include Digital Media Forensic Detection (11 papers), Advanced Steganography and Watermarking Techniques (4 papers) and Generative Adversarial Networks and Image Synthesis (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (623 citations), Signal Processing (77 citations) and Artificial Intelligence (189 citations). Francesco Marra has collaborated with scholars based in Italy. Frequent co-authors include Luisa Verdoliva, Diego Gragnaniello, Giovanni Poggi, Davide Cozzolino, Carlo Sansone, Cristiano Saltori, Giulia Boato and Fabio Roli. Their work appears in journals such as Pattern Recognition Letters, IEEE Transactions on Information Forensics and Security and Multimedia Tools and Applications.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.