The document discusses a web-based service for detecting image tampering, detailing various types of tampering such as splicing, inpainting, and copy-move. It outlines the development of algorithms for tampering localization and evaluation through quantitative and qualitative methodologies, highlighting existing limitations and future plans for improvement. The tool is available for public testing and aims to incorporate advanced features and deep learning techniques in its future iterations.

1. A Web-based Service for Image
Tampering Detection
Symeon (Akis) Papadopoulos - @sympap
Information Technologies Institute (ITI) /
Centre for Research and Technology Hellas (CERTH)
Experts’ Meeting on Digital Image Authentication and Classification
Dec 6-7, 2017 @ Geneva, Switzerland

2. The problem & the promise
https://www.npr.org/templates/story/story.php?storyId=92442928

3. Types of image tampering
• Splicing
– addition of new objects from a different image
• Inpainting
– Fill in part of image using software, similar effect
as splicing
• Copy-move (cloning)
• Cropping
• Image enhancement
– colour/contrast/brightness/gamma

4. Famous examples
https://www.snopes.com/photos/animals/puertorico.asp

5. Famous examples
http://www.imperfectedblog.com/2015/11/crimes-of-retouching-and-
the-importance-of-community/

6. Famous examples
http://www.telegraph.co.uk/news/worldnews/asia/northkorea/9956422
/North-Korea-Photoshopped-marine-landings-photograph.html

7. Other forms of image-based
misinformation
• Reposting
• False claims/associations
• Computer Generated Imagery
https://www.snopes.com/
photos/people/nepalkids.asp
http://www.gizmodo.co.uk/2015/03/that-viral-photo-of-
todays-solar-eclipse-totally-not-real/
http://www.nydailynews.com/news/national
/n-y-post-settles-libel-lawsuit-bag-men-
front-page-story-article-1.1961732

8. Tampering localization algorithms
Splicing
localization
Copy-move
detection
Block
matching
Keypoint
matching
High-frequency
noise
CFA
patterns
JPEG compression
traces
Filtering and
analysis
Camera-
specific (PRNU)
Quantization
artefacts
Compression grid
misalignment
JPEG ghosts,
ELA

9. REVEAL Media Verification Assistant
http://reveal-mklab.iti.gr/
Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y., Bouwmeester, R., & Spangenberg, J. (2016, April). Web
and Social Media Image Forensics for News Professionals. In SMN@ ICWSM.

10. Background
• Development funded by FP7 REVEAL project (2013-
2016)
• Core development team
– Dr. Markos Zampoglou (lead researcher)
– Dr. Chryssanthi Iakovidou (CAGI algorithm)
– Olga Papadopoulou (back-end development)
– Lazaros Apostolidis (front-end development)
• Iterative design and evaluation with the help of Ruben
Bouwmeester and colleagues from DW
• Second round of testing as part of the InVID plug-in
• Further development and support by MKLAB

11. Features
• Tampering localization heat maps
– Based on six state-of-the-art algorithms and one
newly proposed (CAGI)
– Zoom-in and overlay of heat map over image
• Auxiliary features
– Metadata: full listing, GPS geolocation, Exif
thumbnail extraction
– Reverse image search: auto-generation of link to
perform search on Google Images

12. Comparison with other solutions
FotoForensics1 Forensically2 Ghiro3 REVEAL
ELA X X X X
Ghost X
DW Noise X
Median Noise X X
Block Artifact X
Double Quantization X
Copy-move X*
Thumbnail X X
Metadata X X X X
Geotagging X X X X
Reverse search X
1 http://fotoforensics.com
2 http://29a.ch/photo-forensics/
3 http://www.imageforensic.org/
*Forensically implements a very simple block-matching algorithm with low robustness

13. Usage distribution
France 401
Netherlands 262
Germany 214
UK 181
US 153
Argentina 96
Egypt 52

14. Evaluation methodology
• Quantitative
– Six reference datasets (images + binary masks of
tampering = “ground truth”)
– Measures capturing the matching between
ground truth mask and algorithm output
– Comparison of 14 algorithms, “best” six plus a
newly proposed one ended up in the tool
• Qualitative
– Informal feedback has been received by end users
– Pertains to both usability and quality of results
Zampoglou, M., Papadopoulos, S., & Kompatsiaris, Y. (2017). Large-scale evaluation of splicing localization
algorithms for web images. Multimedia Tools and Applications, 76(4), 4801-4834.

15. Limitations
• Currently no support for:
– “tampering probability” score
– single heat map (combining all individual maps in
the best possible way)
– annotation
• Results often unreliable
– very common with images from social media
(Twitter, Facebook) or for images that have been
resaved many times and/or are in poor resolution
– maps are often hard to interpret and leave doubt
Zampoglou, M., Papadopoulos, S., & Kompatsiaris, Y. (2015, June). Detecting image splicing in the wild
(web). In Multimedia & Expo Workshops (ICMEW), 2015 IEEE International Conference on (pp. 1-6). IEEE.

16. Usage & plans
• Public version is free to use “as is” for testing and
demo purposes
– no technical support, no SLA
• Plans
– add a tampering probability score
– generate a “merged” tampering heat map
– test new approaches based on deep learning
– implement usability improvements
• Funding is sought to support our research and
development roadmap

17. Relevant projects from our team
Tweet Verification Assistant
http://reveal-mklab.iti.gr/reveal/fake/
Disturbing Image Content Detection
http://reveal-mklab.iti.gr/reveal/disturbing/
Context Analysis & Aggregation
http://caa.iti.gr/
Reverse Video Search
In progress…

18. Thank you!
http://reveal-mklab.iti.gr/
https://github.com/MKLab-ITI/image-forensics
Get in touch!
Akis Papadopoulos papadop@iti.gr
Markos Zampoglou markzampoglou@iti.gr

19. References
• Bianchi, T, De Rosa, A. & Piva, A. (2011). Improved DCT coefficient analysis for forgery localization in
JPEG images. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
pp. 2444-2447. IEEE, 2011
• Pasquale, F., Bianchi, T., De Rosa, A., & Piva, A. (2012). Image forgery localization via fine-grained
analysis of CFA artifacts. IEEE Transactions on Information Forensics and Security 7(5): 1566-1577
• Farid, H. (2009). Exposing digital forgeries from JPEG ghosts. IEEE Transactions on Information
Forensics and Security 4(1): 154-160.
• Fontani, M., Bianchi, T., De Rosa, A., Piva, A., & Barni, M. (2013). A framework for decision fusion in
image forensics based on dempster–shafer theory of evidence. IEEE Transactions on Information
Forensics and Security 8(4): 593-607.
• Lin, Z., He, J., Tang, X., & Tang, C.-K. (2009). Fast, automatic and fine-grained tampered JPEG image
detection via DCT coefficient analysis. Pattern Recognition 42(11): 2492-2501.
• Mahdian, B. & Saic, S. (2009). Using noise inconsistencies for blind image forensics. Image and
Vision Computing 27(10), pp. 1497–1503.
• Zampoglou, M., Papadopoulos, S., & Kompatsiaris, Y. (2015, June). Detecting image splicing in the
wild (web). In IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 1-6.
• Zampoglou, M., Papadopoulos, S., Kompatsiaris, Y., Bouwmeester, R., & Spangenberg, J. (2016,
April). Web and Social Media Image Forensics for News Professionals. In SMN@ ICWSM
• Zampoglou, M., Papadopoulos, S., & Kompatsiaris, Y. (2017). Large-scale evaluation of splicing
localization algorithms for web images. Multimedia Tools and Applications, 76(4), 4801-4834