Detecting child sexual abuse material: A comprehensive survey
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Releasing datasets containing CSAM material prior to assessment in the name of openness would pose a clear risk.
“For instance, in the case of dealing with datasets that contain CSAM material, the release of data prior to assessment in the name of openness would pose a clear risk [4, 31].”
From Rethinking open source generative AI: open washing and the EU AI Act (2024) · cites this paper as [31]
Lee et al. [17] provided a comprehensive survey on child sexual abuse material detection, focusing on distribution methods, policy and legal framework dimensions, and detection applications and implementations.
“For instance, Lee et al. [17] provided a comprehensive survey on child sexual abuse material detection.The main focus was distribution methods, policy and legal framework dimensions, and detection applications and implementations.”
From Online grooming detection: A comprehensive survey of child exploitation in chat logs (2023) · cites this paper as [17]
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Cryptography and Network Security: Principles and Practice
Stallings, W. (1998) · Cited by 4,407 · see citing works
A Guide to Conducting a Systematic Literature Review of Information Systems Research
Okoli, C., & Schabram, K. (2011) · doi:10.2139/ssrn.1954824 · Cited by 1,849 · see citing works
Proceedings of the IEEE International Conference on Image Processing
Calway, A. D. (1996) · The Institute of Electrical and Electronics Engineers · Cited by 1,566 · see citing works
Proceedings. IEEE International Conference on Multimedia and Expo
(2002) · doi:10.1109/icme.2002 · Cited by 838 · see citing works
Child Sexual Abuse: Consequences and Implications
Hornor, G. (2010) · Journal of Pediatric Health Care · doi:10.1016/j.pedhc.2009.07.003 · Cited by 209 · see citing works
Proceedings of the 16th International Conference on Availability, Reliability and Security
Alessandro Aldini, A. S., & Aldini, A. (2021) · doi:10.1145/3465481 · Cited by 158 · see citing works
Mitchell, K. J., David Finkelhor, B., Jones, L. M., & Wolak, J. (2010) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2010.01.007 · Cited by 106 · see citing works
Child pornography on the Internet
Wortley, R., & SMALLBONE, S. W. (2006) · Cited by 98 · see citing works
Measuring a year of child pornography trafficking by U.S. computers on a peer-to-peer network
Wolak, J., Liberatore, M., & Levine, B. N. (2013) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2013.10.018 · Cited by 90 · see citing works
The Darknet and the Future of Content Protection
Biddle, P., ENGLAND, P. J., Peinado, M., et al. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-44993-5_10 · Cited by 89 · see citing works
Leveraging deep neural networks to fight child pornography in the age of social media
Vitorino, P. R. R., Avila, S., Pérez, M., & Rocha, A. D. R. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.12.005 · Cited by 87 · see citing works
Child pornography in peer-to-peer networks
Steel, C. M. S. (2009) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2008.12.011 · Cited by 83 · see citing works
Automatic detection of child pornography using color visual words
Ulges, A., & Stahl, A. (2011) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2011.6011977 · Cited by 74 · see citing works
Internet Sex Crimes Against Minors: The Response of Law Enforcement.
Wolak, J., Mitchell, K. J., & Finkelhor, D. (2003) · University of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · Cited by 66 · see citing works
Pornography and child sexual abuse detection in image and video: a comparative evaluation
Gangwar, A., Fidalgo, E., Alegre, E., et al. (2017) · International Conference on Imaging for Crime Detection and Prevention (ICDP) · doi:10.1049/ic.2017.0046 · Cited by 62 · see citing works
Schryen, G., Wagner, G., & Benlian, A. (2015) · University of Regensburg Publication Server (University of Regensburg) · doi:10.5283/epub.32455 · Cited by 57 · see citing works
Steel, C. M. S. (2015) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2014.12.009 · Cited by 54 · see citing works
NuDetective: A Forensic Tool to Help Combat Child Pornography through Automatic Nudity Detection
Polastro, M. D., & da Silva Eleuterio, P. M. (2010) · 2010 Workshops on Database and Expert Systems Applications · doi:10.1109/dexa.2010.74 · Cited by 52 · see citing works
Towards automatic detection of child pornography
Sae-Bae, N., Sun, X., Sencar, H. T., et al. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026079 · Cited by 49 · see citing works
Liking and hyperlinking: Community detection in online child sexual exploitation networks
Westlake, B. G., & Bouchard, M. (2016) · Social Science Research · doi:10.1016/j.ssresearch.2016.04.010 · Cited by 47 · see citing works
Bissias, G. D., Levine, B. N., Liberatore, M., Lynn, B., Moore, J., Wallach, H., & Wolak, J. (2015) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2015.10.022 · Cited by 45 · see citing works
Comparing Methods for Detecting Child Exploitation Content Online
Westlake, B. G., Bouchard, M., & Frank, R. W. (2012) · European Intelligence and Security Informatics Conference · doi:10.1109/eisic.2012.25 · Cited by 37 · see citing works
Burglary of Retail Establishments
Clarke, R. V. (2003) · United States. Department of Justice. Office of Community Oriented Policing Services · Cited by 35 · see citing works
Westlake, B. G., Bouchard, M., & Frank, R. W. (2015) · Sexual Abuse · doi:10.1177/1079063215616818 · Cited by 30 · see citing works
A Benchmark Methodology for Child Pornography Detection
Macedo, J., Costa, F., & dos Santos, J. A. (2018) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2018.00065 · Cited by 29 · see citing works
Automatic Detection of CSA Media by Multi-modal Feature Fusion for Law Enforcement Support
SCHULZE, C. H., Henter, D., Borth, D., et al. (2014) · doi:10.1145/2578726.2578772 · Cited by 26 · see citing works
Mutawa, N. A., Bryce, J., Franqueira, V. N. L., et al. (2015) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2015.49 · Cited by 25 · see citing works
Pornography detection in video benefits (a lot) from a multi-modal approach
Ulges, A., SCHULZE, C. H., Borth, D., et al. (2012) · doi:10.1145/2390214.2390222 · Cited by 25 · see citing works
iCOP: Automatically Identifying New Child Abuse Media in P2P Networks
Peersman, C., SCHULZE, C. H., Rashid, A., et al. (2014) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2014.27 · Cited by 25 · see citing works
Strategies to Disrupt Online Child Pornography Networks
Joffres, K., Bouchard, M., Frank, R. W., et al. (2011) · European Intelligence and Security Informatics Conference · doi:10.1109/eisic.2011.32 · Cited by 24 · see citing works
How to Look: Apprehension, Forensic Craft, and the Classification of Child Exploitation Images
Thakor, M. (2017) · IEEE Annals of the History of Computing · doi:10.1109/mahc.2017.25 · Cited by 24 · see citing works
Trends in Online Child sexual abuse material
(2019) · Human Rights Documents online · doi:10.1163/2210-7975_hrd-9926-20180017 · Cited by 21 · see citing works
On the detection of images containing child-pornographic material
Yiallourou, E., Demetriou, R., & Lanitis, A. (2017) · International Conference on Telecommunications · doi:10.1109/ict.2017.7998260 · Cited by 19 · see citing works
Horsman, G. (2018) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2018.07.009 · Cited by 19 · see citing works
“So why did you do it?” Explanations provided by child pornography offenders
Merdian, H. L., Wilson, N. J., Thakker, J., Curtis, C., & Boer, D. (2013) · Lincoln Repository (University of Lincoln) · Cited by 18 · see citing works
A Statistical Approach for Identifying Videos of Child Pornography at Crime Scenes
Polastro, M., & Eleuterio, P. M. D. S. (2012) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1109/ares.2012.71 · Cited by 18 · see citing works
Evaluation of local descriptors and CNNs for non-adult detection in visual content
Castrillón-Santana, M., Lorenzo-Navarro, J., Travieso, C. M., et al. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.03.016 · Cited by 17 · see citing works
Proceedings 2018 European Intelligence and Security Informatics Conference
(2018) · European Intelligence and Security Informatics Conference · doi:10.1109/eisic.2018.00002 · Cited by 17 · see citing works
An adaptive sampling strategy for automatic detection of child pornographic videos
Eleuterio, P. M. D. S., & Polastro, M. (2012) · International Conference on Forensic Computer Science (ICoFCS) · doi:10.5769/c2012002 · Cited by 16 · see citing works
Clandestine methamphetamine labs
Scott, M. S., & Dedel, K. (2006) · PsycEXTRA Dataset · doi:10.1037/e486802008-001 · Cited by 12 · see citing works
Cybersecurity and the Blockchain: Preventing the Insertion of Child Pornography Images
Cremona, K., Tabone, D., & Raffaele, C. D. (2019) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2019.00042 · Cited by 11 · see citing works
Face to Iris Area Ratio as a feature for children detection in digital forensics applications
Chatzis, V., Panagiotopoulos, F., & Mardiris, V. A. (2016) · doi:10.1109/dmiaf.2016.7574915 · Cited by 10 · see citing works
Howard, T. E. (2004) · bepress Legal Repository · doi:10.15779/z38r382 · Cited by 9 · see citing works
Proceedings of the ACM workshop on Digital rights management
(2006) · doi:10.1145/1179509 · Cited by 9 · see citing works
On the Presence of Child Sex Abuse in BitTorrent Networks
Shavitt, Y., & Zilberman, N. (2013) · IEEE Internet Computing · doi:10.1109/mic.2013.2 · Cited by 8 · see citing works
An investigation into darknets and the content available via anonymous peer-to-peer file sharing
Aked, S. (2011) · Australasian Journal of Paramedicine · doi:10.4225/75/57b52857cd8b3 · Cited by 7 · see citing works
How to Look: Apprehension, Forensic Craft, and the Classification of Child Exploitation Images
Thakor, M. (2017) · IEEE Annals of the History of Computing · doi:10.1353/ahc.2017.0008 · Cited by 7 · see citing works
An Illustration of Hashing and Its Effect on Illegal File Content in the Digital AgePreprint
Hoffman, S. P. (2010) · SSRN Electronic Journal · Cited by 5 · see citing works
Efficient Tagging of Remote Peers during Child Pornography Investigations
Liberatore, M., Levine, B. N., Shields, C., et al. (2013) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2013.46 · Cited by 5 · see citing works
New approaches to dealing with online child pornography
Carr, J. N. (2011) · Cited by 3 · see citing works
Kong, A. W. (2015) · IEEE International WIE Conference on Electrical and Computer Engineering · doi:10.1109/wiecon-ece.2015.7444022 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Child Sexual Abuse and the Internet—A Systematic Review
Ali, S., Haykal, H. A., & Youssef, E. Y. M. (2023) · Human Arenas · doi:10.1007/s42087-021-00228-9 · Cited by 77 · see citing works
Rethinking open source generative AI: open washing and the EU AI Act
Liesenfeld, A., & Dingemanse, M. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3659005 · Cited by 67 · see citing works
“For instance, in the case of dealing with datasets that contain CSAM material, the release of data prior to assessment in the name of openness would pose a clear risk [4, 31].”
BackgroundOnline grooming detection: A comprehensive survey of child exploitation in chat logs
Borj, P. R., Raja, K., & Bours, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110039 · Cited by 38 · see citing works
Hamming distributions of popular perceptual hashing techniques
McKeown, S., & Buchanan, W. J. (2023) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2023.301509 · Cited by 15 · see citing works
Conway, P., Redmond, T., Lundrigan, S., Davy, D., Bailey, S., & Lee, P. (2024) · Depression and Anxiety · doi:10.1155/da/1854312 · Cited by 14 · see citing works
- Authors
- Lee, H.-E., Ermakova, T., Ververis, V., & Fabian, B.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- Fraunhofer Institute for Open Communication Systems (DE)
- Corresponding author
- Ermakova, Tatiana
- Venue
- Forensic Science International: Digital Investigation
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 34
- Pages
- 301022
- ISSN
- 2666-2817 (ISSN-L)
- OpenAlex
- W3044564311
- Cited by
- 80
- References
- 52
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 14 · 2023: 25 · 2022: 7 · 2021: 8 · 2020: 2
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Forensic Science International Digital Investigation (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Information Systems › Digital and Cyber Forensics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Keywords
- child pornography, online child sexual exploitation, web crawling, visual detection, deep learning, revictimization, legal and policy frameworks, distribution channels
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review