Saurabh Agarwal
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Information Systems top 10%
- Electrical and Electronic Engineering
- Media Technology top 10%
- Co-authors
- Satish ChandRajesh KumarKi‐Hyun JungDilip Kumar SharmaHyunsung KimJohn T. NovakMohammad Abu‐OrfA. Sujil
- Topics
- Digital Media Forensic Detection (29 papers)Advanced Steganography and Watermarking Techniques (24 papers)Generative Adversarial Networks and Image Synthesis (8 papers)
- Journals
- SHILAP Revista de lepidopterologíaWater ResearchScientific Reports
- Partner nations
- IndiaSouth KoreaUnited States
In The Last Decade
Saurabh Agarwal
75 papers receiving 467 citations
Peers
Comparison fields: 5 of 90
- Computer Vision and Pattern Recognition 153
- Artificial Intelligence 119
- Information Systems 52
- Electrical and Electronic Engineering 50
- Media Technology 49
Countries citing papers authored by Saurabh Agarwal
This map shows the geographic impact of Saurabh Agarwal'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 Saurabh Agarwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saurabh Agarwal more than expected).
Fields of papers citing papers by Saurabh Agarwal
This network shows the impact of papers produced by Saurabh Agarwal. 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 Saurabh Agarwal. The network helps show where Saurabh Agarwal may publish in the future.
Co-authorship network of co-authors of Saurabh Agarwal
This figure shows the co-authorship network connecting the top 25 collaborators of Saurabh Agarwal. A scholar is included among the top collaborators of Saurabh Agarwal 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 Saurabh Agarwal. Saurabh Agarwal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 11 | |
| 3 | 1 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 5 | |
| 8 | 2 | |
| 9 | 2 | |
| 10 | 5 | |
| 11 | 7 | |
| 12 | 7 | |
| 13 | 3 | |
| 14 | 1 | |
| 15 | 2 | |
| 16 | 26 | |
| 17 | Adaptive Gradient Communication via Critical Learning Regime Identification | 5 |
| 18 | 9 | |
| 19 | Anti-Forensics of JPEG Images using Interpolation | 2 |
| 20 | Image Forgery Detection using Multi Scale Entropy Filter and Local Phase Quantization | 19 |
About Saurabh Agarwal
Saurabh Agarwal is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Energy Engineering and Power Technology, having authored 88 papers that have together received 496 indexed citations. Recurring topics across this work include Digital Media Forensic Detection (29 papers), Advanced Steganography and Watermarking Techniques (24 papers) and Generative Adversarial Networks and Image Synthesis (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (153 citations), Media Technology (49 citations) and Artificial Intelligence (119 citations). Saurabh Agarwal has collaborated with scholars based in India, South Korea and United States. Frequent co-authors include Satish Chand, Rajesh Kumar, Ki‐Hyun Jung, Dilip Kumar Sharma, Hyunsung Kim, John T. Novak, Mohammad Abu‐Orf, A. Sujil, Amel Ali Alhussan and Hanaa A. Abdallah. Their work appears in journals such as SHILAP Revista de lepidopterología, Water Research and Scientific Reports.
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.