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Digital Media Forensic Detection

Digital Media Forensic Detection is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 21k research works in it since 1954. 16.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the detection and identification of digital image forgeries, including techniques such as copy-move forgery detection, sensor pattern noise analysis, JPEG compression history estimation, camera model identification, splicing detection, and tampering localization. The papers also explore the application of deep learning methods for image forensics and the detection of inconsistencies in image manipulation.

  • Image Forgery Detection
  • Digital Forensics
  • Copy-Move Forgery
  • Sensor Pattern Noise
  • JPEG Compression
  • Camera Model Identification
  • Deep Learning
  • Splicing Detection
  • Tampering Localization
  • Resampling Detection
Research works
21k
fractional, since 1954
In the world top 10%
3.6k
per year above
Top-10% rate
16.8%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+72%
the tick is no change

Which countries lead Digital Media Forensic Detection research?

By volume, China and India publish the most (2.2k and 1.5k works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.2k works
  2. 2 India 1.5k works
  3. 3 United States 499 works
  4. 4 South Korea 168 works
  5. 5 United Kingdom 143 works
  6. 6 Italy 126 works
  7. 7 Taiwan 119 works
  8. 8 Indonesia 118 works
  9. 9 Iraq 116 works
  10. 10 Germany 106 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 42.9%India: 30.0%United States: 9.7%South Korea: 3.3%6 others listed: 14.2%43%largest
China2,205 · 42.9%India1,541 · 30.0%United States499 · 9.7%South Korea168 · 3.3%6 others listed729 · 14.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Digital Media Forensic Detection research?

By volume in 2022–2025, Beijing University of Posts and Telecommunications publishes the most Digital Media Forensic Detection research, followed by Vellore Institute of Technology University and University of Science and Technology of China.

Who are the leading researchers in Digital Media Forensic Detection?

The most-cited researchers publishing on Digital Media Forensic Detection include Luc Van Gool, Chen Change Loy and Ming–Hsuan Yang.

  1. 1 Luc Van Gool Switzerland 8.5k citations
  2. 2 Chen Change Loy Singapore 7.7k citations
  3. 3 Ming–Hsuan Yang United States 7.4k citations
  4. 4 Thomas S. Huang United States 7.4k citations
  5. 5 Anil K. Jain United States 6.3k citations
  6. 6 Yann LeCun United States 5.4k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Digital Media Forensic Detection research done?

The largest centres of Digital Media Forensic Detection research in 2022–2025 are Beijing (China), Shanghai (China), Chennai (India) and Nanjing (China).

Largest cities, 2022–2025

  1. 1 Beijing China 397 works
  2. 2 Shanghai China 171 works
  3. 3 Chennai India 125 works
  4. 4 Nanjing China 124 works
  5. 5 Xi'an China 121 works
  6. 6 Guangzhou China 121 works
  7. 7 Seoul South Korea 95 works
  8. 8 Chengdu China 86 works
  9. 9 Hangzhou China 81 works
  10. 10 Bengaluru India 81 works
See Digital Media Forensic Detection on the map

Where is the best place to study Digital Media Forensic Detection?

Among universities, judged by research, Delhi Technological University, Thapar Institute of Engineering & Technology and National Institute of Technology Patna score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 19.58%fractional works in this node (log) →share in the world top 10% →Delhi Technological University: 28, 16.4%Thapar Institute of Engineering & Technology: 11, 29.7%National Institute of Technology Patna: 11, 44.1%Nanjing University of Information Science and Technology: 25, 15.0%Beijing University of Posts and Telecommunications: 46, 15.1%University of Science and Technology of China: 36, 27.5%Amrita Vishwa Vidyapeetham: 36, 12.7%Vellore Institute of Technology University: 37, 13.8%Institute of Engineering: 13, 4.1%Shenzhen University: 27, 17.4%National Institute o…Thapar Institute of …Delhi Technological …Nanjing University o…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Delhi Technological UniversityIndia 64.916.4%15.4×28 +216.2%
2 Thapar Institute of Engineering & TechnologyIndia 64.129.7%10.6×11 +1221.2%
3 National Institute of Technology PatnaIndia 63.244.1%11.9×11
4 Nanjing University of Information Science and TechnologyChina 62.815.0%9.1×25 +475.1%
5 Beijing University of Posts and TelecommunicationsChina 62.015.1%11.1×46 +55.4%
6 University of Science and Technology of ChinaChina 56.627.5%4.5×36 +112.9%
7 Amrita Vishwa VidyapeethamIndia 56.512.7%10.0×36 +34.0%
8 Vellore Institute of Technology UniversityIndia 55.913.8%5.7×37 +416.5%
9 Institute of EngineeringNepal 53.94.1%10.3×13 +175.5%
10 Shenzhen UniversityChina 53.717.4%5.4×27 +162.6%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Digital Media Forensic Detection research growing?

Output in 2018–2022 was 72% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Digital Media Forensic Detection.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.