Facial deepfake performance evaluation based on three detection tools: MTCNN, Dlib, and MediaPipe

Nada Hussein M. Ali

doi:10.1063/5.0213294

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Publication Date

Mon Jan 01 2024

Journal Name

Fifth International Conference On Applied Sciences: Icas2023

Volume

3097

Issue Number

1

DOI

10.1063/5.0213294

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Facial deepfake performance evaluation based on three detection tools: MTCNN, Dlib, and MediaPipe

DeepFake

DeepFake Detection

Face Detection

MTCNN

Dlib

MediaPipe

Nada Hussein M. Ali

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Publication Date

Tue Jan 01 2013

Journal Name

Photonics & Lasers In Medicine

The assessment of pathological changes in cerebral blood flow in hypertensive rats with stress-induced intracranial hemorrhage using Doppler OCT: Particularities of arterial and venous alterations/Die Beurteilung von pathologischen Veränderungen der Hirndurchblutung bei hypertensiven Ratten mit Stress-induzierten intrakraniellen Blutungen mittels Doppler-OCT: Besonderheiten von arteriellen und venösen Veränderungen

Oxana V.

Vladislav V.

Olga A.

Igor A.

Sergey S.

Ekaterina M.

Mohhanad M.

Hans A.

Fatema

Leith

Valery V.

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Abstract<p>Hemorrhagic insult is a major source of morbidity and mortality in both adults and newborn babies in the developed countries. The mechanisms underlying the non-traumatic rupture of cerebral vessels are not fully clear, but there is strong evidence that stress, which is associated with an increase in arterial blood pressure, plays a crucial role in the development of acute intracranial hemorrhage (ICH), and alterations in cerebral blood flow (CBF) may contribute to the pathogenesis of ICH. The problem is that there are no effective diagnostic methods that allow for a prognosis of risk to be made for the development of ICH. Therefore, quantitative assessment of CBF may significantly advance the underst</p> ... Show More

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Publication Date

Mon Oct 13 2025

Journal Name

Mesopotamian Journal Of Cybersecurity

Improvement of the Face Recognition Systems Security Against Morph Attacks using the Developed Siamese Neural Network

Face Recognition Systems (FRS)

Siamese Neural Network (SNN)

Faster Region-based Convolutional Neural Networks (R-CNN)

Local Binary Pattern-Convolutional Neural Network (LBP-CNN)

Deep Learning

Morphing Attacks

Sura

Thair

Nada

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Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

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