A Deep-Learning Enabled Traffic Analysis Engine for Video Source Identification

2019 ◽  
Author(s):  
Yan Shi ◽  
Subir Biswas
Keyword(s):  
Deep Learning ◽  
Source Identification ◽  
Traffic Analysis ◽  
Video Source ◽  
Analysis Engine

scholarly journals Machine Learning-Based Malicious X.509 Certificates’ Detection

2021 ◽  
Vol 11 (5) ◽  
pp. 2164
Author(s):  
Jiaxin Li ◽  
Zhaoxin Zhang ◽  
Changyong Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Ensemble Learning ◽  
Traffic Analysis ◽  
Learning Models ◽  
Detection Model ◽  
Analysis Tools ◽  
Average Accuracy ◽  
Machine Learning Models

X.509 certificates play an important role in encrypting the transmission of data on both sides under HTTPS. With the popularization of X.509 certificates, more and more criminals leverage certificates to prevent their communications from being exposed by malicious traffic analysis tools. Phishing sites and malware are good examples. Those X.509 certificates found in phishing sites or malware are called malicious X.509 certificates. This paper applies different machine learning models, including classical machine learning models, ensemble learning models, and deep learning models, to distinguish between malicious certificates and benign certificates with Verification for Extraction (VFE). The VFE is a system we design and implement for obtaining plentiful characteristics of certificates. The result shows that ensemble learning models are the most stable and efficient models with an average accuracy of 95.9%, which outperforms many previous works. In addition, we obtain an SVM-based detection model with an accuracy of 98.2%, which is the highest accuracy. The outcome indicates the VFE is capable of capturing essential and crucial characteristics of malicious X.509 certificates.

scholarly journals Hybrid reference-based Video Source Identification

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 649 ◽  
Author(s):  
Massimo Iuliani ◽  
Marco Fontani ◽  
Dasara Shullani ◽  
Alessandro Piva
Keyword(s):  
Social Media ◽  
Source Identification ◽  
State Of The Art ◽  
Visual Media ◽  
Multimedia Forensics ◽  
Reference Pattern ◽  
Still Images ◽  
Video Source ◽  
Video Frames ◽  
Social Media Platforms

Millions of users share images and videos generated by mobile devices with different profiles on social media platforms. When publishing illegal content, they prefer to use anonymous profiles. Multimedia Forensics allows us to determine whether videos or images have been captured with the same device, and thus, possibly, by the same person. Currently, the most promising technology to achieve this task exploits unique traces left by the camera sensor into the visual content. However, image and video source identification are still treated separately from one another. This approach is limited and anachronistic, if we consider that most of the visual media are today acquired using smartphones that capture both images and videos. In this paper we overcome this limitation by exploring a new approach that synergistically exploits images and videos to study the device from which they both come. Indeed, we prove it is possible to identify the source of a digital video by exploiting a reference sensor pattern noise generated from still images taken by the same device. The proposed method provides performance comparable with or even better than the state-of-the-art, where a reference pattern is estimated from video frames. Finally, we show that this strategy is effective even in the case of in-camera digitally stabilized videos, where a non-stabilized reference is not available, thus solving the limitations of the current state-of-the-art. We also show how this approach allows us to link social media profiles containing images and videos captured by the same sensor.

Video source identification in lossy wireless networks

2013 ◽  
Author(s):  
Shaxun Chen ◽  
Amit Pande ◽  
Kai Zeng ◽  
Prasant Mohapatra
Keyword(s):  
Wireless Networks ◽  
Source Identification ◽  
Video Source

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.

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
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