BIOGRAPHY
Dr. Aanchal Agarwal obtained her B.Tech degree in Electronics and Communication from State University in 2010. Building upon her undergraduate education, she pursued an M.Tech (Gold-Medalist), in Electronics and Communication specializing in Communication Systems in 2018. Driven by a passion for research, she continued her academic journey and successfully completed her Ph.D. degree in Electronics and Communication from JIIT Sector-62 Noida. Her doctoral research focused on image forensics, employing advanced techniques such as machine learning and deep learning. She authored four research papers, three of which are SCIE indexed, and one is SCOPUS indexed. This demonstrates her commitment to exploring cutting-edge technologies to address contemporary challenges in the field of multimedia forensics. Her research interests are image processing, video processing, machine learning, deep learning, and natural language processing (NLP).
EDUCATIONAL QUALIFICATIONS:
- Ph.D in Electronics and Communication department, JIIT Sector 62, Noida, Uttar Pradesh.
- M.Tech in Electronics and Communication specializing in Communication Systems, JIIT Sector 62, Noida (GOLD MEDALIST)
- B.Tech in Electronics and Communication from State University, Lucknow, Uttar Pradesh
INTEREST AREA:
Multimedia Forensics, Machine Learning, Deep learning, Computer vision, Natural language processing (NLP).
JOURNAL PUBLICATIONS:
- Agarwal, A., Khandelwal, V. Multiple Manipulation Detection in Images Using Frequency Domain Features in 3D-CNN. Arab J Sci Eng (2023). https://doi.org/10.1007/s13369-023-07727-7 (SCIE indexed Impact factor: 2.807)
- Agarwal, A., Gupta, A. Efficacy of Residual Methods for Passive Image Forensics Using Four Filtered Residue CNN. SN COMPUT. SCI. 3, 491 (2022). https://doi.org/10.1007/s42979-022-01396-3 (SCOPUS Impact factor: 1.29)
- Agarwal, Aanchal & Gupta, Abhinav. (2022). Real-time double JPEG forensics for mobile devices. Journal of Real-Time Image Processing. 1-11. https://doi.org/10.1007/s11554-022-01218-y (SCI Indexed, Impact factor: 3.03)
- Agarwal, A., Gupta, A. A maximum relevancy and minimum redundancy feature selection approach for median filtering forensics. Multimed. Tools Appl. 79, 21743–21770 (2020). https://doi.org/10.1007/s11042-020-08994-4 SCI Indexed, Impact factor: 3.6)