| Blur‐invariant copy‐move forgery detection technique with improved detection accuracy utilising SWT‐SVD R Dixit, R Naskar, S Mishra IET Image Processing 11 (5), 301-309, 2017 | 71 | 2017 |
| Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between Haralick coded frames J Bakas, R Naskar, R Dixit Multimedia Tools and Applications 78, 4905-4935, 2019 | 52 | 2019 |
| Automated image splicing detection using deep CNN-learned features and ANN-based classifier S Nath, R Naskar Signal, Image and Video Processing 15 (7), 1601-1608, 2021 | 51 | 2021 |
| Review, analysis and parameterisation of techniques for copy–move forgery detection in digital images R Dixit, R Naskar IET Image Processing 11 (9), 746-759, 2017 | 46 | 2017 |
| A digital forensic technique for inter–frame video forgery detection based on 3D CNN J Bakas, R Naskar International Conference on Information Systems Security, 304-317, 2018 | 36 | 2018 |
| Reversible watermarking utilising weighted median-based prediction R Naskar, RS Chakraborty IET Image Processing 6 (5), 507-520, 2012 | 36 | 2012 |
| Camera Source Identification Using Discrete Cosine Transform Residue Features and Ensemble Classifier. A Roy, RS Chakraborty, VU Sameer, R Naskar CVPR workshops, 1848-1854, 2017 | 31 | 2017 |
| Detection and localization of inter-frame forgeries in videos based on macroblock variation and motion vector analysis J Bakas, R Naskar, S Bakshi Computers & Electrical Engineering 89, 106929, 2021 | 28 | 2021 |
| A generalized tamper localization approach for reversible watermarking algorithms R Naskar, RS Chakraborty ACM Transactions on Multimedia Computing, Communications, and Applications …, 2013 | 25 | 2013 |
| Deep siamese network for limited labels classification in source camera identification VU Sameer, R Naskar Multimedia Tools and Applications 79 (37), 28079-28104, 2020 | 24 | 2020 |
| Histogram‐bin‐shifting‐based reversible watermarking for colour images R Naskar, R Subhra Chakraborty IET Image Processing 7 (2), 99-110, 2013 | 22 | 2013 |
| Copy-move forgery detection exploiting statistical image features R Dixit, R Naskar, A Sahoo 2017 international conference on wireless communications, signal processing …, 2017 | 21 | 2017 |
| A deep learning model for depression detection based on MFCC and CNN generated spectrogram features AK Das, R Naskar Biomedical Signal Processing and Control 90, 105898, 2024 | 20 | 2024 |
| Computer vision approach for phase identification from steel microstructure A Choudhury, S Pal, R Naskar, A Basumallick Engineering Computations 36 (6), 1913-1933, 2019 | 19 | 2019 |
| Improved histogram bin shifting based reversible watermarking P Nagarju, R Naskar, RS Chakraborty 2013 International Conference on Intelligent Systems and Signal Processing …, 2013 | 19 | 2013 |
| A 3D wide residual network with perceptual loss for brain MRI image denoising A Panda, R Naskar, S Rajbans, S Pal 2019 10th International Conference on Computing, Communication and …, 2019 | 18 | 2019 |
| Digital image forensics A Roy, R Dixit, R Naskar, RS Chakraborty Springer, 2020 | 17 | 2020 |
| Deep learning approach for segmentation of plain carbon steel microstructure images A Panda, R Naskar, S Pal IET Image Processing 13 (9), 1516-1524, 2019 | 17 | 2019 |
| Classification of Computer Generated and Natural Images based on Efficient Deep Convolutional Recurrent Attention Model. DB Tariang, P Senguptab, A Roy, RS Chakraborty, R Naskar CVPR workshops, 146-152, 2019 | 17 | 2019 |
| Mpeg double compression based intra-frame video forgery detection using cnn J Bakas, AK Bashaboina, R Naskar 2018 International Conference on Information Technology (ICIT), 221-226, 2018 | 16 | 2018 |
Rajat Subhra ChakrabortyProfessor, Dept. of Computer Science and Engineering, IIT Kharagpur, India - 721302Bestätigte E-Mail-Adresse bei cse.iitkgp.ac.in
