Ruchira Naskar

955 total citations
69 papers, 516 citations indexed

About

Ruchira Naskar is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Biophysics. According to data from OpenAlex, Ruchira Naskar has authored 69 papers receiving a total of 516 indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Computer Vision and Pattern Recognition, 19 papers in Media Technology and 8 papers in Biophysics. Recurrent topics in Ruchira Naskar's work include Digital Media Forensic Detection (46 papers), Advanced Steganography and Watermarking Techniques (38 papers) and Image Processing Techniques and Applications (17 papers). Ruchira Naskar is often cited by papers focused on Digital Media Forensic Detection (46 papers), Advanced Steganography and Watermarking Techniques (38 papers) and Image Processing Techniques and Applications (17 papers). Ruchira Naskar collaborates with scholars based in India, Italy and Russia. Ruchira Naskar's co-authors include Rajat Subhra Chakraborty, Snehanshu Pal, Sambit Bakshi, A. Basumallick, Aditi Sahoo, Amitava Choudhury, Michele Nappi, Sarmistha Neogy, Indranil Sengupta and Dinesh Kumar Mishra and has published in prestigious journals such as Pattern Recognition Letters, IEEE Signal Processing Letters and Biomedical Signal Processing and Control.

In The Last Decade

Ruchira Naskar

60 papers receiving 489 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ruchira Naskar India 12 421 117 55 49 45 69 516
Ngoc‐Son Vu France 11 397 0.9× 64 0.5× 6 0.1× 17 0.3× 47 1.0× 28 467
Abhishek Thakur India 10 118 0.3× 32 0.3× 19 0.3× 22 0.4× 50 1.1× 63 344
Jumana Waleed Iraq 11 141 0.3× 25 0.2× 6 0.1× 9 0.2× 72 1.6× 53 304
Yotam Nitzan Israel 9 479 1.1× 14 0.1× 7 0.1× 18 0.4× 76 1.7× 14 574
E. S. Gopi India 8 86 0.2× 24 0.2× 7 0.1× 4 0.1× 61 1.4× 35 228
Ambuj Mehrish India 7 81 0.2× 14 0.1× 8 0.1× 4 0.1× 103 2.3× 18 278
Tetsuya Matsumoto Japan 10 61 0.1× 25 0.2× 4 0.1× 4 0.1× 88 2.0× 63 305
Jarmila Pavlovičová Slovakia 11 217 0.5× 23 0.2× 5 0.1× 47 1.0× 53 364
Ninad Thakoor United States 10 209 0.5× 38 0.3× 14 0.3× 38 0.8× 41 313
Seungjoon Yang South Korea 12 546 1.3× 187 1.6× 4 0.1× 36 0.8× 54 636

Countries citing papers authored by Ruchira Naskar

Since Specialization
Citations

This map shows the geographic impact of Ruchira Naskar'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 Ruchira Naskar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruchira Naskar more than expected).

Fields of papers citing papers by Ruchira Naskar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ruchira Naskar. 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 Ruchira Naskar. The network helps show where Ruchira Naskar may publish in the future.

Co-authorship network of co-authors of Ruchira Naskar

This figure shows the co-authorship network connecting the top 25 collaborators of Ruchira Naskar. A scholar is included among the top collaborators of Ruchira Naskar 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 Ruchira Naskar. Ruchira Naskar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Naskar, Ruchira, et al.. (2025). Kol-4-Gen: Stacked Kolmogorov-Arnold and Generative Adversarial Networks for Malware Binary Classification Through Visual Analysis. IEEE Embedded Systems Letters. 17(4). 268–271. 1 indexed citations
3.
Naskar, Ruchira, et al.. (2024). Image splicing detection using low-dimensional feature vector of texture features and Haralick features based on Gray Level Co-occurrence Matrix. Signal Processing Image Communication. 125. 117134–117134. 9 indexed citations
4.
Naskar, Ruchira, et al.. (2024). Can Deepfakes Mimic Human Emotions? A Perspective on Synthesia Videos. 306–309. 1 indexed citations
5.
Neogy, Sarmistha, et al.. (2024). Deep Learning-based forgery detection and localization for compressed images using a hybrid optimization model. Multimedia Systems. 30(3). 3 indexed citations
6.
Naskar, Ruchira, et al.. (2023). A deep learning model for depression detection based on MFCC and CNN generated spectrogram features. Biomedical Signal Processing and Control. 90. 105898–105898. 31 indexed citations
7.
Naskar, Ruchira, et al.. (2023). Image splicing detection with principal component analysis generated low-dimensional homogeneous feature set based on local binary pattern and support vector machine. Multimedia Tools and Applications. 82(17). 25847–25864. 7 indexed citations
8.
Naskar, Ruchira, et al.. (2021). Object-based forgery detection in surveillance video using capsule network. Journal of Ambient Intelligence and Humanized Computing. 14(4). 3781–3791. 8 indexed citations
9.
Naskar, Ruchira, et al.. (2020). Deep siamese network for limited labels classification in source camera identification. Multimedia Tools and Applications. 79(37-38). 28079–28104. 19 indexed citations
10.
Naskar, Ruchira, et al.. (2020). Malware Classification Through Attention Residual Network based Visualization. 1–6. 3 indexed citations
11.
Chakraborty, Rajat Subhra, et al.. (2019). A Robust Residual Dense Neural Network For Countering Antiforensic Attack on Median Filtered Images. IEEE Signal Processing Letters. 26(8). 1132–1136. 8 indexed citations
12.
Chakraborty, Rajat Subhra, et al.. (2019). Classification of Computer Generated and Natural Images based on Efficient Deep Convolutional Recurrent Attention Model.. Computer Vision and Pattern Recognition. 146–152. 9 indexed citations
13.
Naskar, Ruchira, et al.. (2018). MPEG Double Compression Based Intra-Frame Video Forgery Detection using CNN. 221–226. 10 indexed citations
14.
Naskar, Ruchira, et al.. (2018). Copy–move forgery detection utilizing Fourier–Mellin transform log-polar features. Journal of Electronic Imaging. 27(2). 1–1. 10 indexed citations
15.
Chakraborty, Rajat Subhra, et al.. (2017). Camera Source Identification Using Discrete Cosine Transform Residue Features and Ensemble Classifier. 1848–1854. 19 indexed citations
16.
Naskar, Ruchira, et al.. (2016). DyWT based copy-move forgery detection with improved detection accuracy. 133–138. 1 indexed citations
17.
Naskar, Ruchira & Rajat Subhra Chakraborty. (2015). A Technique to Evaluate Upper Bounds on Performance of Pixel–prediction Based Reversible Watermarking Algorithms. Journal of Signal Processing Systems. 82(3). 373–389. 4 indexed citations
18.
Naskar, Ruchira, et al.. (2015). A tamper localization approach for reversible watermarking based on histogram bin shifting. 16. 721–726. 3 indexed citations
19.
Naskar, Ruchira & Rajat Subhra Chakraborty. (2014). Reversible Digital Watermarking: Theory and Practices. CERN Document Server (European Organization for Nuclear Research). 5(1). 1–130. 3 indexed citations
20.
Naskar, Ruchira, et al.. (2013). Improved histogram bin shifting based reversible watermarking. 62–65. 7 indexed citations

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.

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