scholarly journals Video Source Identification Algorithm Based on 3D Geometric Transformation

2020 ◽  
Vol 35 (6) ◽  
pp. 513-521
Author(s):  
Jian Li ◽  
Yang Lv ◽  
Bin Ma ◽  
Meihong Yang ◽  
Chunpeng Wang ◽  
...  
Keyword(s):  
Source Identification ◽  
Geometric Transformation ◽  
Identification Algorithm ◽  
Video Source

A Source Identification Algorithm for INTEGRAL

2008 ◽  
Author(s):  
Simone Scaringi ◽  
Antony J. Bird ◽  
David J. Clark ◽  
Anthony J. Dean ◽  
Adam B. Hill ◽  
...  
Keyword(s):  
Source Identification ◽  
Identification Algorithm

Time-Series Segmentation Using Predictive Modular Neural Networks

1997 ◽  
Vol 9 (8) ◽  
pp. 1691-1709 ◽  
Author(s):  
Athanasios Kehagias ◽  
Vassilios Petridis
Keyword(s):  
Time Series ◽  
Source Identification ◽  
Predictive Accuracy ◽  
Identification Algorithm ◽  
Time Series Classification ◽  
Time Step ◽  
Modular Neural Networks ◽  
Time Series Segmentation ◽  
Network Method

A predictive modular neural network method is applied to the problem of unsupervised time-series segmentation. The method consists of the concurrent application of two algorithms: one for source identification, the other for time-series classification. The source identification algorithm discovers the sources generating the time series, assigns data to each source, and trains one predictor for each source. The classification algorithm recursively computes a credit function for each source, based on the competition of the respective predictors, according to their predictive accuracy; the credit function is used for classification of the time-series observation at each time step. The method is tested by numerical experiments.

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.

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