Deep learning based forensic face verification in videos

Zeng, Jinhua; Zeng, Jinfeng; Qiu, Xiulian

doi:10.1109/pic.2017.8359518

Cited by 15 publications

(7 citation statements)

References 12 publications

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“…In addition, there are applications in forensics that deal with, for example, the identification of image sources, forensic face verification in videos, drowning diagnosis or the interpretation of gunshot wounds [ 32 , 33 , 34 , 35 ]. The input for CNNs/FCNs/U-Nets is an n-dimensional matrix; it can thus be used for classification and clustering of image data.…”

Section: Usage Of Neural Network For Medical Data Analysismentioning

confidence: 99%

“…Artificial neural networks have shown their potential particularly for the analysis of image data. For example, forensic scientists use this technology for drowning diagnosis, face verification, image manipulation detection or gunshot wound interpretation [ 32 , 33 , 34 , 35 ]. Other researchers in the field of source image forensics are looking at issues of source camera identification, forensics of re-recorded images, computer graphics image forensics, GAN-generated image recognition and source social network identification [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

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Applications of Neural Networks in Biomedical Data Analysis

et al. 2022

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Neural networks for deep-learning applications, also called artificial neural networks, are important tools in science and industry. While their widespread use was limited because of inadequate hardware in the past, their popularity increased dramatically starting in the early 2000s when it became possible to train increasingly large and complex networks. Today, deep learning is widely used in biomedicine from image analysis to diagnostics. This also includes special topics, such as forensics. In this review, we discuss the latest networks and how they work, with a focus on the analysis of biomedical data, particularly biomarkers in bioimage data. We provide a summary on numerous technical aspects, such as activation functions and frameworks. We also present a data analysis of publications about neural networks to provide a quantitative insight into the use of network types and the number of journals per year to determine the usage in different scientific fields.

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Section: Usage Of Neural Network For Medical Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications of Neural Networks in Biomedical Data Analysis

et al. 2022

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“…Artificial visando solucionar problemas de autenticação nos sistemas de monitoramento de instituições públicas e privadas. Visão Computacional tem sido amplamente utilizada em sistemas de monitoramento, como mecanismo de autenticação [8], [9], [10], [11], [12] e de detecção de presença de pessoas [13], [14], [15]. Os autores em [9] expõem um protótipo de reconhecimento facial baseado em um estudo feito sobre o algoritmo de Viola Jones, que foi desenvolvido especificamente para o treinamento de redes neurais para reconhecimento de rostos de pessoas.…”

Section: Nos úLtimos Anos Diversos Pesquisadores Têm Mostrado Interesse Em Mecanismos De Inteligênciaunclassified

Sistema Embarcado Para Identificação De Alunos Uniformizados Utilizando Visão Computacional No Raspberry Pi

Santos¹,

Bueno²,

Oliveira³

et al. 2021

Sistema Embarcado Para Identificação De Alunos Uniformizados Utilizando Visão Computacional No Raspberry Pi

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“…Human-computer interaction increasingly relies on Machine Learning (ML) models such as Deep Neural Networks (DNNs) trained from, usually large, datasets [1,2,3,4]. The ubiquitous applications of DNNs in security-critical tasks, such as face identity recognition [5,6], speaker verification [7,8], voice controlled systems [9,10,11] or signal forensics [12,13,14,15] require a high reliability on these computational models. However, it has been demonstrated that such models can be fooled by perturbing an input sample with malicious and quasi-imperceptible perturbations.…”

Section: Introductionmentioning

confidence: 99%

On the human evaluation of audio adversarial examples

Vadillo¹,

Santana²

2020

Preprint

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Human-machine interaction is increasingly dependent on speech communication. Machine Learning models are usually applied to interpret human speech commands. However, these models can be fooled by adversarial examples, which are inputs intentionally perturbed to produce a wrong prediction without being noticed. While much research has been focused on developing new techniques to generate adversarial perturbations, less attention has been given to aspects that determine whether and how the perturbations are noticed by humans. This question is relevant since high fooling rates of proposed adversarial perturbation strategies are only valuable if the perturbations are not detectable. In this paper we investigate to which extent the distortion metrics proposed in the literature for audio adversarial examples, and which are commonly applied to evaluate the effectiveness of methods for generating these attacks, are a reliable measure of the human perception of the perturbations. Using an analytical framework, and an experiment in which 18 subjects evaluate audio adversarial examples, we demonstrate that the metrics employed by convention are not a reliable measure of the perceptual similarity of adversarial examples in the audio domain.

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Deep learning based forensic face verification in videos

Cited by 15 publications

References 12 publications

Applications of Neural Networks in Biomedical Data Analysis

Applications of Neural Networks in Biomedical Data Analysis

Sistema Embarcado Para Identificação De Alunos Uniformizados Utilizando Visão Computacional No Raspberry Pi

On the human evaluation of audio adversarial examples

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