Deep learning for printed document source identification

2019 ◽  
Vol 70 ◽  
pp. 184-198 ◽  
Author(s):  
Min-Jen Tsai ◽  
Yu-Han Tao ◽  
Imam Yuadi
Keyword(s):  
Deep Learning ◽  
Source Identification

scholarly journals Spatial and temporal learning representation for end-to-end recording device identification

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunyan Zeng ◽  
Dongliang Zhu ◽  
Zhifeng Wang ◽  
Minghu Wu ◽  
Wei Xiong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Source Identification ◽  
Spatial Information ◽  
Temporal Information ◽  
Recording Device ◽  
Spatial Feature ◽  
Baseline System ◽  
Feature Information ◽  
End To End ◽  
Temporal Feature

AbstractDeep learning techniques have achieved specific results in recording device source identification. The recording device source features include spatial information and certain temporal information. However, most recording device source identification methods based on deep learning only use spatial representation learning from recording device source features, which cannot make full use of recording device source information. Therefore, in this paper, to fully explore the spatial information and temporal information of recording device source, we propose a new method for recording device source identification based on the fusion of spatial feature information and temporal feature information by using an end-to-end framework. From a feature perspective, we designed two kinds of networks to extract recording device source spatial and temporal information. Afterward, we use the attention mechanism to adaptively assign the weight of spatial information and temporal information to obtain fusion features. From a model perspective, our model uses an end-to-end framework to learn the deep representation from spatial feature and temporal feature and train using deep and shallow loss to joint optimize our network. This method is compared with our previous work and baseline system. The results show that the proposed method is better than our previous work and baseline system under general conditions.

A Deep Learning Approach to MRI Scanner Manufacturer and Model Identification

2020 ◽  
Vol 2020 (4) ◽  
pp. 217-1-217-7
Author(s):  
Shengbang Fang ◽  
Ronnie A. Sebro ◽  
Matthew C. Stamm
Keyword(s):  
Deep Learning ◽  
Digital Image ◽  
Source Identification ◽  
Medical Records ◽  
Model Identification ◽  
Diagnostic Techniques ◽  
Experimental Results ◽  
Learning Approach ◽  
Scientific Fraud ◽  
Mri Image

Forensics research has developed several techniques to identify the model and manufacturer of a digital image or videos source camera. However, to the best of our knowledge, no work has been performed to identify the manufacturer and model of the scanner that captured an MRI image. MRI source identification can have several important applications ranging from scientific fraud discovery, exposing issues around anonymity and privacy of medical records, protecting against malicious tampering of medical images, and validating AI-based diagnostic techniques whose performance varies on different MRI scanners. In this paper, we propose a new CNN-based approach to learn forensic traces left by an MRI scanner and use these traces to identify the manufacturer and model of the scanner that captured an MRI image. Additionally, we identify an issue called weight divergence that can occur when training CNNs using a constrained convolutional layer and propose three new correction functions to protect against this. Our experimental results show we can identify an MRI scanners manufacturer with 97.88% accuracy and its model with 91.07% accuracy. Additionally, we show that our proposed correction functions can noticeably improve our CNNs accuracy when performing scanner model identification.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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