Dealing with video source identification in social networks

2017 ◽  
Vol 57 ◽  
pp. 1-7 ◽  
Author(s):  
Irene Amerini ◽  
Roberto Caldelli ◽  
Andrea Del Mastio ◽  
Andrea Di Fuccia ◽  
Cristiano Molinari ◽  
...  
Keyword(s):  
Social Networks ◽  
Source Identification ◽  
Video Source

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

Video source identification from MP4 data based on field values in atom/box attributes

2021 ◽  
Keyword(s):  
International Symposium ◽  
Source Identification ◽  
Fast Track ◽  
Video Source ◽  
Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Media Watermarking, Security, and Forensics 2021 proceedings.

Rumor Source Identification in Social Networks with Time-Varying Topology

2018 ◽  
Vol 15 (1) ◽  
pp. 166-179 ◽  
Author(s):  
Jiaojiao Jiang ◽  
Sheng Wen ◽  
Shui Yu ◽  
Yang Xiang ◽  
Wanlei Zhou
Keyword(s):  
Social Networks ◽  
Source Identification ◽  
Time Varying

scholarly journals Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

2023 ◽  
Vol 55 (1) ◽  
pp. 1-51
Author(s):  
Huacheng Li ◽  
Chunhe Xia ◽  
Tianbo Wang ◽  
Sheng Wen ◽  
Chao Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Source Identification ◽  
Information Diffusion ◽  
Viral Marketing ◽  
Diffusion Modeling ◽  
Diffusion Dynamics ◽  
Information User ◽  
Pros And Cons ◽  
Open Issues

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

scholarly journals Digital Video Source Identification Using Sensor Pattern Noise with Morphology Filtering

2017 ◽  
Vol 6 (1) ◽  
pp. 15-22 ◽  
Author(s):  
Sang-Hyeong Lee ◽  
Dong-Hyun Kim ◽  
Tae-Woo Oh ◽  
Ki-Bom Kim ◽  
Hae-Yeoun Lee
Keyword(s):  
Source Identification ◽  
Digital Video ◽  
Video Source ◽  
Pattern Noise ◽  
Sensor Pattern Noise
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