The document discusses multimedia forensics, focusing on techniques for source identification and integrity verification of digital media. It highlights the importance of identifying inherent traces in digital media to verify authenticity and the challenges posed by modern image editing techniques. Various methods of forensic analysis, including the assessment of image manipulation and the impact of social media on image history, are explored.

1. Multimedia Forensics:
discovering the history of
multimedia contents.
Prof. Sebastiano Battiato
Dipartimento di Matematica e Informatica,
Università di Catania
Image Processing LAB – http://iplab.dmi.unict.it
battiato@dmi.unict.it
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

2. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
Multimedia
Forensics
- Source identification
- Integrity verification/tampering detection
Techniques from multimedia forensics merely provide a way to
test for the authenticity and source of digital sensor data. In this
sense is not about analyzing the semantics of digital or
digitized media objects.

3. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

4. Original File: Special Cases
• Recapture: create a fake and then take a
picture with the camera we want to
pretend the picture was taken with
• Staging: the image file is authentic, but
the content has been staged
In these cases an authentic file does not
imply an authentic content.

5. Multimedia Forensics
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

6. Multimedia Forensics (in practice)
• Source Identification
• Integrity/Authenticity
• Enhancement/Restoration
• Interpretation and Content Analysis
– Plate Recognition
– Dynamic Reconstruction (car crashes, etc.)
– Antropomethric issues
– …
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

7. “Forensics Image (Video) analysis is
the application of IMAGE SCIENCE
and DOMAIN EXPERTISE to interpret
the content of an image or the image
itself in legal matters” (SWGIT –
www.fbi.gov)
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

8. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
Recent documents:
• 2016-02-08 SWGDE Best Practices for Photographic Comparison for All
Disciplines
• 2016-02-08 SWGDE Image Processing Guidelines Version1.0
• 2016-02-08 SWGDE Proposed Techniques for Advanced Data Recovery
from Security Digital Video Recorders v1-1
• 2016-02-08 SWGDE Training Guidelines for Video Analysis, Image
Analysis and Photography V1-1
https://www.swgde.org/

9. ISO Guidelines
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

10. Fantasy/Fiction
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

11. CSI Effect
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

12. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

13. Esper Blade Runner
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

14. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

15. Reality
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

16. I Need That Plate! No Way...

17. I Need That Plate! No Way...

18. Boston Marathon
“The FBI, reportedly has more than 2,000 agents looking at the publicly
available evidence,”

19. Challenging Problems
Prof. Sebastiano Battiato – CF 2015-2016

21. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
(source Interpol)

22. Multimedia Forensics is based on the idea
that inherent traces (like digital fingerprints)
are left behind in a digital media during both
the creation phase and any other
successively process.
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

23. • Example:
• Forensic analysis of a smartphone: which pictures have been generated
on the device and which ones have been generated by other devices
and sent by messaging application or saved from the internet
• We can identify:
• Type of device
• Maker and model
• Specific exemplar
Camera Ballistics
Which Device Has Created This Picture?

24. Device Identification

25. Model Identification
http://snapsnapsnap.photos/how-does-the-iphone-6-camera-compare-to-previous-iphone-cameras/

26. Camera Identification

27. Source Identification Noise Based
Sensor output carries not only pure signal
but also various noise components. Sensor
noise model could be used as a
representative feature for cameras.
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

28. PRNU as a camera fingerprint

29. PRNU Estimation
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

30. Sensor Identification Using
Pattern Noise
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
[Lukas2006] J. Lukas, J. Fridrich, and M. Goljan, “Digital Camera identification from sensor pattern noise” IEEE
Transaction Inf. Foren.Sec. Vol. 1, 205–214 (2006).

31. Sensor Identification Using
Pattern Noise
This method provide good results, and is
quite reliable also using:
–images with different level of JPEG
compression (low, medium)
–images processed using point-wise operator
such as brightness/contrast adjustment or
gamma correction.
–images acquired by two cameras of the same
brand and model.
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

32. Integrity: What is a Forgery?
• “Forgery” is a
subjective word.
• An image can
become a forgery
based upon the
context in which
it is used.
• An image altered for fun or someone who has taken an bad
photo, but has been altered to improve its appearance
cannot be considered a forgery even though it has been
altered from its original capture.
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

33. Altering Images
The concepts have moved
into the digital world by
virtue of digital cameras
and the availability of
digital image editing
software
The ease of use of digital image editing software, which does
not require any special skills, makes image manipulation easy
to achieve.
circa 1860: This nearly iconic portrait of U.S. President
Abraham Lincoln is a composite of Lincoln's head and the
Southern politician John Calhoun's body.
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

34. Who Cares?
media
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

35. Who Cares?
geopolitics
…
…and political propaganda2nd Meeting EU IAI – Interpol Headquarter
(Lyon) – October 2016

36. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

37. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
Advertisement

38. More (and more) examples
Photo Tampering through History
http://www.fourandsix.com/photo-tampering-history/
Photoshopdisaster
http://www.photoshopdisasters.com/
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

39. Image Editing
 Malicious image editing alters the image semantic
content, mainly:
 Adding information
 Removing information
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
Piva 2013

40. Image Editing
• Splicing (two images)
– Also called cut and paste, compositing
– Used to add information
• Cloning (single image)
– Also called copy and paste, copy move, region duplication
– Used to add or remove information
– Can be exact, or the clone can be resized, rotated…)
• Inpainting (kind of intelligent clone)
– Seam carving, content aware resize, content aware fill, content dependent crop
– Used to remove information
• Retouch (local editing)
– Dodge and burn, healing tool…
• Image enhancement/filtering
– Histogram equalization, contrast enhancement, median filtering, denoise, smooth…
• Image editing (geometric transformation)
– Resize, crop, zoom, shear
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

41. How To Authenticate An Image?
• Visual Inspection
• File Analysis
File Format and Structures
Metadata (EXIF)
Compression Parameters (Quantization
Tables)
• Global Analysis
Pixel and compressed data statistics
• Local Analysis
Finding inconsistencies of pixel statistics
across the image

42. Image Forensics Methods
Passive Methods: Using the alterations of
the underlying statistics produced by digital
forgeries on an image:
PHYSICS BASED
CAMERA BASED
PIXEL BASED
GEOMETRIC BASED
FORMAT BASED
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

43. Physics-Based
Lighting inconsistencies can used for revealing traces of
digital tampering.
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

44. Camera-Based
INTERPOLATION
LENS CFA SENSOR
POST
PROCESSINGDIGITAL IMAGESTORAGE
Processing and
Storage
ORIGINAL
IMAGE
Acquisition
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

45. Types Of Analysis: Signal Level
Based on statistical features of pixel values; need good quality image
• Clone detection
– Cloned image blocks
– Similar couples of key points
• Resampling detection
– For resize, rotate, but also when splicing or cloning
• Enhancement Detection
– Specific for algorithms (median, histogram equalization, color
adjustment)
• Seam carving detection
• General intrinsic footprints
• Inconsistencies from acquisition and coding fingerprints
– CFA, PRNU, DCT, ELA…
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

46. Format-Based
JPEG compression engine
(for both luminance and chrominance channels):
the input image is
partitioned into 8x8
non-overlapping blocks
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

47. Format-Based
JPEG forgery engine
2nd Meeting EU IAI – Interpol Headquarter
(Lyon) – October 2016

48. Periodic artifact introduced by
Double JPEG quantizations (2)
A. C. Popescu and H. Farid, Statistical tools for digital forensics, in Proc. 6th Int. Workshop Information Hiding, Berlin,
Germany, 2004, pp. 128–147, Springer-Verlag.
Z. Lin, J. He, X. Tang, and C.-K. Tang, Fast, automatic and fine-grained tampered JPEG image detection via DCT
coefficient analysis, Pattern Recognition, vol. 42, no. 11, pp. 2492–2501, Nov. 2009.
If q2<q1, then n(u2) =0 for some u2, hence the histogram related to the double
quantization can show periodically missing values. On the contrary, if q2>q1 the
histogram can have some periodicity in terms of peaks and valleys pattern.
2nd Meeting EU IAI – Interpol Headquarter
(Lyon) – October 2016

49. THE TYPICAL PIPELINE
FOR A COPY-PASTE
OPERATION
+
=
original image
QF(1) = q1
resulting image
QF(3) = q3
2nd image
QF(2) = q2
duplicating
resizing
2nd Meeting EU IAI – Interpol Headquarter
(Lyon) – October 2016

50. F. Galvan, G. Puglisi, A. R. Bruna, S. Battiato, First Quantization Matrix Estimation from Double Compressed JPEG
Images, IEEE Transactions on Information Forensics and Security, 2014.
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

51. Image Alignment for Tampering Detection
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
S. Battiato, G. M. Farinella, E. Messina, G. Puglisi - Robust Image Alignment for Image
Authentication and Tampering Detection – IEEE Transactions on Information Forensics & Security,
Vol. 7 – Issue 4, pp. 1105-1117, 2012.

52. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
http://revealproject.eu/
http://www.rewindproject.eu/
http://maven-project.eu/#_=_
https://s-five.eu/
The final draft of the FIVE Best Practice
Manual is publically available from
December 8, 2015 ("October/DIWG2015
version"): DRAFT_BPM_FIVE_20151009

53. Use Cases
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

54. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

55. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

56. Image Manipulation: Case “Mozzarella Blu”
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

57. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

58. Evidence on the web
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

59. Forgery on Biomedical Images
Corriere della Sera – Ottobre 2013
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

60. Forgery on Science
“What’s in a picture? The temptation of image manipulation.,” J. Cell Biol., vol. 166, no. 1, pp.
11–5, Jul. 2004.
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

61. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
Current Trends And
Challenges

62. Current Trends: Point&Shoot
and Share…
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

63. Future of Imaging
Nikon

64. Sharing

65. The Social Picture
S. Battiato, G. M. Farinella, F. L. M. Milotta, A. Ortis, L. Addesso, A. Casella, V. D'amico, G. Torrisi, The Social
Picture, ACM International Conference on Multimedia Retrieval 2016

66. 2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016
Social (Multimedia) Forensics
• Image and Video Phylogeny
ReVeal project

67. Social (Multimedia) Forensics
• Uploading an image on a Social Network
- The process alters images
- Resize
- Rename
- Meta-Data deletion/editing
- Re-Compression
- NEW JPEG file Structure
M. Moltisanti, A. Paratore, S. Battiato, L. Saravo - Image Manipulation on Facebook for Forensics
Evidence – ICIAP 2015, LNCS 2015;
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of

68. Social (Multimedia) Forensics (2)
• Uploading an image on a Social Network
- The process alters images
- Each Social Network Service do different alterations
Resized
Proportionally
Squared
Image
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

69. Social (Multimedia) Forensics (2)
• Uploading an image on a Social Network
- The process alters images
- Each Social Network Service makes different
alterations
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

70. Social (Multimedia) Forensics (2)
• Uploading an image on a Social Network
- The process alters images
- Each Social Network Service makes different
alterations
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

71. Social (Multimedia) Forensics (2)
• Uploading an image on a Social Network
- The process alters images
- Each Social Network Service makes different
alterations

72. Social (Multimedia) Forensics (2)
• Uploading an image on a Social Network
- The process alters images
- Each Social Network Service makes different
alterations
Social Network
Fingerprint
on Uploaded
Images

73. Social (Multimedia) Forensics (2)
• Uploading an image on a Social Network
- The process alters images
- Each Social Network Service makes different
alterations
- Alterations are dependent to uploading client

74. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image history)
Uploaded images
dataset

75. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image history)
Social Altered image dataset
- 10 Social Platforms
- Facebook, Google+, Instagram, Flickr, Tumblr,
Twitter, Imgur, Tinypic, Telegram, Whatsapp
- 2720 JPEG Images representing different
subjects (natural, indoor, outdoor)
- Dataset available at:
http://iplab.dmi.unict.it/DigitalForensics/social_image_forensics/

76. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image history)
- On which Social Network was uploaded image I?
I Given a JPEG image I, the Social Image Ballistics task has the objective of
defining:
1) if there is a compatibility between the non-related JPEG elements of I
(i.e. filename, EXIF data) and the processing pipeline of SNSs;
2) if there is a compatibility between the JPEG elements of I and the
processing pipeline of SNSs;
3) which SNS is compatible with the JPEG elements of the image, with a
certain degree of confidence, and what is the uploading source in
terms of operating system (OS) and application.
Input Image
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

77. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image history)
- On which Social Network was uploaded image I?
I
Input Image
Feature
Extraction
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

78. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image history)
- On which Social Network was uploaded image I?
I
Input Image
Feature
Extraction
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

79. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image
history)
- On which Social Network was uploaded image I?
Representation of whole Dataset
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

80. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image history)
- On which Social Network was uploaded image I?
Input Image
Feature
Extraction
• DQTs coeffs
• Image Size
• # EXIF
• # JPEG Markers
Anomaly
Detection
The Anomaly Detector excludes images not processed
by Social Network Platforms
Given a Similarity measure between features extracted
from images:
It is possible to build a distance matrix D of size N×N
where the element dij is equal to the distance
between the images Ii and Ij.
The Anomaly Detector is then defined as:

81. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image history)
- On which Social Network was uploaded image I?
Input Image
Feature
Extraction
• DQTs coeffs
• Image Size
• # EXIF
• # JPEG Markers
Anomaly
Detection
SNS
Classification
Upload Client
Classification
Output: Not in our dataset
The image probably is not altered by a SNS
Image does not come from considered platforms
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

82. Social (Multimedia) Forensics (2)
• Social Image Ballistics (recover image history)
- On which Social Network was uploaded image I?
O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

83. O. Giudice, A. Paratore, M. Moltisanti, S. Battiato - A Classification Engine for Image Ballistics of
Social Data – (Arxiv 2016 No. 1699257) http://arxiv.org/abs/1610.06347

84. Conclusions
• Multimedia Forensics is now a
consolidated field but new intriguing
challenges emerge every day.
• Among other current trends include:
– Big Data analysis (e.g. Social Network) by
«deep» paradigm?
– Advanced Video Synopsis (First-person-
Vision)
– Semantic Exploitation of user-generated
content
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

85. Surveys
• Matthew C. Stamm, Min Wu and K. J. Ray Liu, Information
Forensics: An Overview of the First Decade (2013), in: IEEE
Access, 1(167-200)
• Alessandro Piva, An Overview on Image Forensics (2013), in:
ISRN Signal Processing, 2013 (Article ID 496701, 22 pages)
• C. Baron - Adobe Photoshop Forensics – Sleuths, Thruts, and
Fauxtography – Thomson Course Tehcnology - 2009
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

86. On line Resources
• Tutorial by Prof. Hany Farid - Digital Image Forensics:
lecture notes, exercises, and matlab code for a survey
course in digital image and video
forensics. http://www.cs.dartmouth.edu/farid/downloads/tut
orials/digitalimageforensics.pdf
• SOFTWARE: Amped5, Authenticate, Adroit, Four&Six,
Izitru, Ghiro, …
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

87. Other related works
• Wang W. and Dong J. and Tan T.: Exploring DCT Coefficient
Quantization Effects for Local Tampering Detection, IEEE
Transactions on Information Forensics and Security, 9, 10,
1653–1666, (2014)
• Liu Q. and Sung A.H. and Chen Z. and Chen L.: Exposing
Image Tampering with the Same Quantization Matrix,
Multimedia Data Mining and Analytics, 327–343, (2015)
• C. Pasquini, F. Perez-Gonzlez, Giulia Boato: A Benford-
Fourier JPEG compression detector. ICIP 2014:
• C. Pasquini, G. Boato, F. Perez-Gonzlez Multiple JPEG
compression detection by means of Benford-Fourier
coefficients. WIFS 2014
2nd Meeting EU IAI – Interpol
Headquarter (Lyon) – October 2016

88. Main Scientific Publications
M.Moltisanti, A.Paratore, S. Battiato, L. Saravo - Image Manipulation on Facebook for
Forensics Evidence – ICIAP 2015, LNCS 2015;
F. Galvan, G. Puglisi, A. R. Bruna, S. Battiato, First Quantization Matrix Estimation from
Double Compressed JPEG Images, IEEE Transactions on Information Forensics and
Security, 2014
S. Battiato, G. M. Farinella, E. Messina, G. Puglisi - Robust Image Alignment for Image
Authentication and Tampering Detection – IEEE Transactions on Information Forensics
& Security, Vol. 7 – Issue 4, pp. 1105-1117, 2012.
S. Battiato, G. M. Farinella, G. Puglisi, D. Ravì – Aligning Codeboooks for Near
Duplicate Image Detection – Multimedia Tools and Applications - Springer 2013.
S. Battiato, G. Messina - Digital Forgery Estimation into DCT Domain - A Critical Analysis
- In Proceedings of ACM Multimedia 2009 - Workshop Multimedia in Forensics - Bejing
(China), October 2009.
S. Battiato, G.M. Farinella, G.C. Guarnera, T. Meccio, G. Puglisi, D. Ravì, R. Rizzo - Bags
of Phrases with Codebooks Alignment for Near Duplicate Image Detection – In
Proceedings of ACM Multimedia – Workshop Multimedia in Forensics, Security and
Intelligence (MiFor 2010) – Florence (Italy), October 2010;
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

89. IISFA memberbook
• S. Battiato, G. Messina, R. Rizzo - Image Forensics - Contraffazione Digitale e
Identificazione della Camera di Acquisizione: Status e Prospettive - Chapter in IISFA
Memberbook 2009 DIGITAL FORENSICS - Eds. G. Costabile, A. Attanasio - Experta, Italy
2009;
• S. Battiato, G.M. Farinella, G. Messina, G. Puglisi - Digital Video Forensics: Status e
Prospettive - Chapter in IISFA Memberbook 2010 DIGITAL FORENSICS - Eds. G. Costabile,
A. Attanasio - Experta, Italy 2010
• S. Battiato, G.M. Farinella, G. Puglisi - Image/Video Forensics: Casi di Studio - Chapter in
IISFA Memberbook 2011 DIGITAL FORENSICS - Eds. G. Costabile, A. Attanasio - Experta,
Italy 2012.
• S. Battiato, M. Moltisanti – Tecniche di Steganografia su Immagini Digitali – Chapter in
IISFA Memberbook 2012 DIGITAL FORENSICS - Eds. G. Costabile, A. Attanasio - Experta,
Italy (2013)
• S.Battiato, F. Galvan, M. Jerian, M. Salcuni - Linee Guida per l'autenticazione Forense di
Immagini – Chapter in IISFA Memberbook 2013 DIGITAL FORENSICS - Eds. G. Costabile,
A. Attanasio - Experta, Italy (2013)
• S. Battiato, A. Catania, F. Galvan, M. Jerian, L.P. Fontana – Acquisizione ed Analisi
Forense di Sistemi di Videosorveglianza - Chapter in IISFA Memberbook 2014 DIGITAL
FORENSICS - Eds. G. Costabile, A. Attanasio - Experta, Italy 2015
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

90. Sicurezza e Giustizia
• S.Battiato, F. Galvan - Introduzione alla Image/Video Forensics - Sicurezza e
Giustizia - Numero I/MMXIII - pp. 42-43 – 2013.
• S.Battiato, F. Galvan - La Validità Probatoria Delle Immagini e dei Video-
Sicurezza e Giustizia - Numero II/MMXIII - pp. 30-31 – 2013
• S.Battiato, F. Galvan - Ricostruzione Di Informazioni 3d A Partire Da Immagini
Bidimensionali - Sicurezza e Giustizia ( n.IV_MMXIII ) – 2014
• S.Battiato, F. Galvan - Verifica dell'Attendibilità di un Alibi Costituito da
Immagini o Video - Sicurezza e Giustizia - Numero II/MMXIV - pp. 47-50 – 2014.
• Rundo, E. Tusa, S. Battiato - Medical Image Enhancement nei Procedimenti
Giudiziari Medico-Legali in ambito Oncologico - Sicurezza e Giustizia - Numero
I/MMXVI - pp. 53-56 - 2016
• - See more at: http://www.sicurezzaegiustizia.com/
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

91. Prof. Sebastiano Battiato
Dipartimento di Matematica e Informatica
University of Catania, Italy
Image Processing LAB – http://iplab.dmi.unict.it
battiato@dmi.unict.it
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016

92. Main Contacts
Further Info
Image Processing Lab
Università di Catania
www.dmi.unict.it/~iplab
Email
battiato@dmi.unict.it
2nd Meeting EU IAI – Interpol Headquarter (Lyon) – October 2016