Joanna Bitton
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence
- Signal Processing
- Safety Research
- Cognitive Neuroscience
- Co-authors
- Brian DolhanskyMenglin WangCristian Canton-FerrerJacqueline PanAlbert GordoCaner HazırbaşCristian Canton FerrerJulian McAuley
- Topics
- Generative Adversarial Networks and Image Synthesis (2 papers)Ethics and Social Impacts of AI (2 papers)Digital Media Forensic Detection (2 papers)
- Journals
- IEEE Transactions on Biometrics Behavior and Identity SciencearXiv (Cornell University)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Partner nations
- CanadaUnited States
In The Last Decade
Joanna Bitton
6 papers receiving 190 citations
Peers
Comparison fields: 5 of 40
- Computer Vision and Pattern Recognition 150
- Artificial Intelligence 74
- Signal Processing 29
- Safety Research 11
- Cognitive Neuroscience 8
Countries citing papers authored by Joanna Bitton
This map shows the geographic impact of Joanna Bitton'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 Joanna Bitton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joanna Bitton more than expected).
Fields of papers citing papers by Joanna Bitton
This network shows the impact of papers produced by Joanna Bitton. 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 Joanna Bitton. The network helps show where Joanna Bitton may publish in the future.
Co-authorship network of co-authors of Joanna Bitton
This figure shows the co-authorship network connecting the top 25 collaborators of Joanna Bitton. A scholar is included among the top collaborators of Joanna Bitton 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 Joanna Bitton. Joanna Bitton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 19 | |
| 3 | 16 | |
| 4 | 8 | |
| 5 | 40 | |
| 6 | The DeepFake Detection Challenge Dataset | 120 |
About Joanna Bitton
Joanna Bitton is a scholar working on Safety Research, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 6 papers that have together received 204 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (2 papers), Ethics and Social Impacts of AI (2 papers) and Digital Media Forensic Detection (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (150 citations), Health Informatics (4 citations) and Signal Processing (29 citations). Joanna Bitton has collaborated with scholars based in Canada and United States. Frequent co-authors include Brian Dolhansky, Menglin Wang, Cristian Canton-Ferrer, Jacqueline Pan, Albert Gordo, Caner Hazırbaş, Cristian Canton Ferrer, Julian McAuley, Farinaz Koushanfar and Ivan Evtimov. Their work appears in journals such as IEEE Transactions on Biometrics Behavior and Identity Science, arXiv (Cornell University) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
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.