research-article

Forensic Image Inspection Assisted by Deep Learning

Authors:

Martin SteinebachAuthors Info & Claims

ARES '17: Proceedings of the 12th International Conference on Availability, Reliability and Security

Article No.: 53, Pages 1 - 9

https://doi.org/10.1145/3098954.3104051

Published: 29 August 2017 Publication History

Abstract

Investigations on the charge of possessing child pornography usually require manual forensic image inspection in order to collect evidence. When storage devices are confiscated, law enforcement authorities are hence often faced with massive image datasets which have to be screened within a limited time frame. As the ability to concentrate and time are highly limited factors of a human investigator, we believe that intelligent algorithms can effectively assist the inspection process by rearranging images based on their content. Thus, more relevant images can be discovered within a shorter time frame, which is of special importance in time-critical investigations of triage character.

While currently employed techniques are based on black- and whitelisting of known images, we propose to use deep learning algorithms trained for the detection of pornographic imagery, as they are able to identify new content. In our approach, we evaluated three state-of-the-art neural networks for the detection of pornographic images and employed them to rearrange simulated datasets of 1 million images containing a small fraction of pornographic content. The rearrangement of images according to their content allows a much earlier detection of relevant images during the actual manual inspection of the dataset, especially when the percentage of relevant images is low. With our approach, the first relevant image could be discovered between positions 8 and 9 in the rearranged list on average. Without using our approach of image rearrangement, the first relevant image was discovered at position 1,463 on average.

References

[1]

Rigan Ap-Apid. 2005. An Algorithm for Nudity Detection. In PCSC'05. Computing Society of the Philippines, Manila, PHL.

[2]

Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, and Vasudeva Varma. 2017. Deep Learning for Hate Speech Detection in Tweets. In WWW '17 Companion. International World Wide Web Conferences Steering Committee, Geneva, CH, 759--760.

Digital Library

[3]

Steven K. Esser, Paul A. Merolla, John V. Arthur, Andrew S. Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J. Berg, Jeffrey L. McKinstry, Timothy Melano, Davis R. Barch, Carmelo di Nolfo, Pallab Datta, Arnon Amir, Brian Taba, Myron D. Flickner, and Dharmendra S. Modha. Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing. In PNAS, Vol. 113. Issue 41.

[4]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, Washington, DC, USA, 1026--1034.

Digital Library

[5]

P. Kakumanu, S. Makrogiannis, and N. Bourbakis. 2007. A survey of skin-color modeling and detection methods. In Pattern Recognition, Vol. 40. 1106--1122.

Digital Library

[6]

Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, Vol. 25. Curran Associates, Inc., New York, 1097--1105.

Digital Library

[7]

Fabio Marturana and Simone Tacconi. 2013. A Machine Learning-based Triage methodology for automated categorization of digital media. In Digital Investigation, Vol. 10. Elsevier, New York, NY, USA, 193--204.

Digital Library

[8]

Henning Müller, Wolfgang Müller, David McG. Squire, Stéphane Marchand-Maillet, and Thierry Pun. 2001. Performance Evaluation in Content-based Image Retrieval: Overview and Proposals. In Pattern Recognition Letters, Vol. 22. Elsevier, New York, NY, USA, 593--601.

Digital Library

[9]

Christian Platzer, Martin Stuetz, and Martina Lindorfer. 2014. Skin Sheriff: A Machine Learning Solution for Detecting Explicit Images. In SFCS '14. 45--56.

Digital Library

[10]

Siddharth Roheda and Hari Kalva. 2017. A Multi-Scale Approach to Skin Pixel Detection. In Journal of Electronic Imaging. Society for Imaging Science and Technology, Springfield, VA, USA, 18--23.

[11]

Masaki Saito and Yusuke Matsui. 2015. Illustration2Vec: A Semantic Vector Representation of Illustrations. In SIGGRAPH Asia 2015 Technical Briefs. ACM, New York, NY, USA, 5:1--5:4.

Digital Library

[12]

Clayton Santos, Eulanda M. dos Santos, and Eduardo Souto. 2012. Nudity Detection Based on Image Zoning. In ISSPA'12. 1098--1103.

[13]

Jin S Seo, Jaap Haitsma, Ton Kalker, and Chang D Yoo. 2004. A robust image fingerprinting system using the Radon transform. Signal Processing: Image Communication 19, 4 (2004), 325--339.

[14]

Simon Thorpe, Denis Fize, and Catherine Marlot. 1996. Speed of processing in the human visual system. In Nature, Vol. 381. Macmillan Publishers, London, UK, 520--522.

[15]

Adrian Ulges and Armin Stahl. 2011. Automatic Detection of Child Pornography Using Color Visual Words. In ICME'11. ACM, Washington, DC, USA, 1--6.

Digital Library

[16]

Ramarathnam Venkatesan, S-MKoon, MariuszH Jakubowski, and Pierre Moulin. 2000. Robust image hashing. In Image Processing, 2000. Proceedings. 2000 International Conference on, Vol. 3. IEEE, New York, NY, USA, 664--666.

[17]

William John Youden. 1950. Index for Rating Diagnostic Tests. In Cancer, Vol. 3. Wiley, New Jersey, USA, 32--35.

Cited By

Park JGracie JAlsoubai AStringhini GSingh VWisniewski P(2023)Towards Automated Detection of Risky Images Shared by Youth on Social MediaCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587607(1348-1357)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587607
Steinebach M(2023)Erkennung von Kindesmissbrauch in MedienDatenschutz und Datensicherheit - DuD10.1007/s11623-023-1750-847:4(225-228)Online publication date: 14-Apr-2023
https://doi.org/10.1007/s11623-023-1750-8
Fachruddin FSaparudin SRasywir EPratama Y(2022)Object Detection Experiment in CBIR Works using Gradation Contour Line2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)10.1109/ISMODE53584.2022.9742892(189-194)Online publication date: 29-Jan-2022
https://doi.org/10.1109/ISMODE53584.2022.9742892
Show More Cited By

Index Terms

Forensic Image Inspection Assisted by Deep Learning
1. Applied computing
  1. Computer forensics
    1. Evidence collection, storage and analysis
    2. Investigation techniques
2. Information systems
  1. Information retrieval

Recommendations

Image retrieval based on bag of images
ICIP'09: Proceedings of the 16th IEEE international conference on Image processing

Conventional relevance feedback schemes may not be suitable to all practical applications of content-based image retrieval (CBIR), since most ordinary users would like to complete their search in a single interaction, especially on the web search. In ...
Image inpainting based on deep learning: A review
Abstract
Image inpainting is an important research direction in the study of computer vision, and is widely used in image editing and photo inpainting etc. Traditional image inpainting algorithms are often difficult to deal with large-scale image deletion,...
Highlights
- Reviewing the image inpainting on deep learning of the past 15 years.
- Summarizing the prominent deep learning methods especially in feature fusion.
- Pointing out the problem and difficulty in image inpainting task.
- Providing ...
The Application of Deep Learning in Image Deblurring
CAIBDA '24: Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms

Images are an important medium for people to obtain information. In the process of acquiring images, it is inevitable that images will become blurred due to various reasons. Image restoration is a technical method that uses algorithms to enhance image ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ARES '17: Proceedings of the 12th International Conference on Availability, Reliability and Security

August 2017

853 pages

ISBN:9781450352574

DOI:10.1145/3098954

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Hessisches Ministerium des Innern und für Sport (HMdIS)

Conference

ARES '17

ARES '17: International Conference on Availability, Reliability and Security

August 29 - September 1, 2017

Reggio Calabria, Italy

Acceptance Rates

ARES '17 Paper Acceptance Rate 100 of 191 submissions, 52%;

Overall Acceptance Rate 228 of 451 submissions, 51%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
287
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)3

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Park JGracie JAlsoubai AStringhini GSingh VWisniewski P(2023)Towards Automated Detection of Risky Images Shared by Youth on Social MediaCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587607(1348-1357)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587607
Steinebach M(2023)Erkennung von Kindesmissbrauch in MedienDatenschutz und Datensicherheit - DuD10.1007/s11623-023-1750-847:4(225-228)Online publication date: 14-Apr-2023
https://doi.org/10.1007/s11623-023-1750-8
Fachruddin FSaparudin SRasywir EPratama Y(2022)Object Detection Experiment in CBIR Works using Gradation Contour Line2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE)10.1109/ISMODE53584.2022.9742892(189-194)Online publication date: 29-Jan-2022
https://doi.org/10.1109/ISMODE53584.2022.9742892
Hales GBayne E(2019)Investigating Visualisation Techniques for Rapid Triage of Digital Forensic EvidenceHCI for Cybersecurity, Privacy and Trust10.1007/978-3-030-22351-9_19(277-293)Online publication date: 12-Jun-2019
https://doi.org/10.1007/978-3-030-22351-9_19

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents