Improving the accuracy of automated facial age estimation to aid CSEM investigations

Anda, Felix; Lillis, David; Kanta, Aikaterini; Becker, Brett A.; Bou‐Harb, Elias; Le-Khac, Nhien-An; Scanlon, Mark

doi:10.1016/j.diin.2019.01.024

Cited by 4 publications

(4 citation statements)

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“…For example, a DF investigation into Child Sexual Exploitation Material (CSEM) [16,17] may seek to automatically detect and classify images of people found on a seized device as adult or underage (based on automatic estimated age). Because of possible misrepresentation in the dataset, misclassification (i.e., false positive), misinterpretation of features, and missing of critical features during the classification process that could have served as evidence (false negative; e.g., an underage wearing adult facial makeup) may occur [18]. In this case, merely addressing bugs in algorithmic codes may not be sufficient, as the classification errors may be subconsciously inherited and propagated through data.…”

Section: Methods For Evaluating Dfai Techniquesmentioning

confidence: 99%

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

Solanke

Biasiotti²

2022

Künstl Intell

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The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning models’ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domain’s adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methods’ admissibility in a judicial process.

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Section: Methods For Evaluating Dfai Techniquesmentioning

confidence: 99%

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

Solanke

Biasiotti²

2022

Künstl Intell

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“…We have studied the possibility of including artificial intelligence as a means to detect and analyse arXiv:1907.01427v1 [cs.CV] 2 Jul 2019 evidence that may be presented in court. Specifically, we have focused on the improvement of facial age estimation algorithms for the identification of victims/suspects and its applications to child sexual exploitation material (CSEM) and child sexual abuse material (CSAM) investigations 1 . Challenges arise due to the factors previously mentioned, thus hampering age classification accuracy, especially for borderline cases between underage and adult subjects.…”

Section: Introductionmentioning

confidence: 99%

“…Child exploitation investigations are one of the more common investigation types in digital forensic laboratories throughout the world [1]. These investigations have become an arduous task due to the increasing usage of anonymization tools, private P2P networks and cloud-based KVM systems [9].…”

Section: Introductionmentioning

confidence: 99%

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Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

Anda

Lillis

Kanta

et al. 2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

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Achieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge. Several studies have used different approaches from the age of a baby to an elder adult and different datasets have been employed to measure the mean absolute error (MAE) ranging between 1.47 to 8 years. The weakness of the algorithms specifically in the borderline has been a motivation for this paper. In our approach, we have developed an ensemble technique that improves the accuracy of underage estimation in conjunction with our deep learning model (DS13K) that has been fine-tuned on the Deep Expectation (DEX) model. We have achieved an accuracy of 68% for the age group 16 to 17 years old, which is 4 times better than the DEX accuracy for such age range. We also present an evaluation of existing cloud-based and offline facial age prediction services, such as Amazon Rekognition, Microsoft Azure Cognitive Services, How-Old.net and DEX.

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The role of evaluations in reaching decisions using automated systems supporting forensic analysis

Bollé

Casey

Jacquet

2020

Forensic Science International: Digital Investigation

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Improving the accuracy of automated facial age estimation to aid CSEM investigations

Cited by 4 publications

References 0 publications

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques

Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

The role of evaluations in reaching decisions using automated systems supporting forensic analysis

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