Face to Iris Area Ratio as a feature for children detection in digital forensics applications

Chatzis, Vassilios; Panagiotopoulos, Fotios; Mardiris, Vassilios A.

doi:10.1109/dmiaf.2016.7574915

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…DIF procedures aid in the facilitation or advancement of reconstructing illicit events. Images depicting children in sexually explicit situations are considered child pornography, which documents child sexual abuse (CSA) [2]. DIF methods like manual inspection, hash sets, percentage of skin tones, faces, and edge detection have all been used to find illicit images.…”

Section: Methodsmentioning

confidence: 99%

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Decoding Innocence: Advancing Forensic Facial Discrimination through Comparative Analysis of Conventional CNN and Advanced Architectures

Ahmed,

Abdullah

2024

JOIV : Int. J. Inform. Visualization

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The field of Digital Image Forensics (DIF) faces a critical issue in accurately identifying children in digital images, notably in cases involving the proliferation of child sexual abuse content. Existing techniques face hurdles due to model architecture limitations, dataset suitability concerns, and classification imbalance, impeding their ability to recognize children to deter pornographic images. Addressing this challenge, this study introduces Implicit Feature Extraction (IFE), a specialized approach for distinguishing child and adult images in object detection. Leveraging Convolutional Neural Networks (CNNs), the IFE method automates the extraction of discriminative facial features, surpassing the constraints of Explicit Feature Extraction (EFE) methods, which achieve an accuracy of around 70%. The research focuses on three core objectives introducing IFE for detailed face feature detection in DIF's child and adult image identification, implementing IFE with CNNs to enhance image classification, and conducting a thorough evaluation of the proposed technique's performance using key metrics like accuracy and balanced classification results and comparing the result with a basic CNN model’s performance. This research's significance lies in its notable contributions to digital image forensics, particularly in combatting child exploitation. The fusion of IFE with CNNs showcases 92% accuracy in distinguishing child and adult images, promising advancements with practical implications in child protection and forensic investigations. The comprehensive evaluation using the UTKFace dataset underscores the proposed technique's efficacy, marking a substantial improvement in child image identification within digital image forensics.

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Section: Methodsmentioning

confidence: 99%

“…However, they fall short when one's position and viewpoint limit one's ability to photograph the whole body. Using biometric ratios and wrinkle analysis, the Haar method divides facial photos into age groups [2].…”

Section: Methodsmentioning

confidence: 99%

Decoding Innocence: Advancing Forensic Facial Discrimination through Comparative Analysis of Conventional CNN and Advanced Architectures

Ahmed,

Abdullah

2024

JOIV : Int. J. Inform. Visualization

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An offline parallel architecture for forensic multimedia classification

Spalazzi

Paolanti

Frontoni

2021

Multimed Tools Appl

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Nowadays, the volume of the multimedia heterogeneous evidence presented for digital forensic analysis has significantly increased, thus requiring the application of big data technologies, cloud-based forensics services, as well as Machine Learning (ML) techniques. In digital forensics domain, ML algorithms have been applied for cybercrime investigation such as child abuse investigations, malware classification, and image forensics. This paper addresses this issues and deals with forensic analysis of digital images and videos. In particular, this work aims at proposing a multimedia classification tool with a parallel software architecture for a fast inspection, which is easy to use (to be used by officers during a search), requires limited hardware resources and it is built on an open-source software to limit its costs. Moreover, this tool must be able to quickly inspect multiple devices at a time. When positives are found in a device, such device will be seized for a deeper analysis later in the lab. It will not be seized otherwise, reducing the inconvenience for the suspect as well as the time required for the next analysis phase. As a case study, we focus on the identification of child pornography images. Experimental results show that the proposed architecture is capable of guaranteeing a high recall, a fast process and high performances in real scenarios.

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Leveraging deep neural networks to fight child pornography in the age of social media

Vitorino

Avila

Pérez

et al. 2018

Journal of Visual Communication and Image Representation

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Face to Iris Area Ratio as a feature for children detection in digital forensics applications

Cited by 4 publications

References 23 publications

Decoding Innocence: Advancing Forensic Facial Discrimination through Comparative Analysis of Conventional CNN and Advanced Architectures

Decoding Innocence: Advancing Forensic Facial Discrimination through Comparative Analysis of Conventional CNN and Advanced Architectures

An offline parallel architecture for forensic multimedia classification

Leveraging deep neural networks to fight child pornography in the age of social media

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