Dealing with video source identification in social networks

Amerini, Irene; Caldelli, Roberto; Mastio, Andrea Del; Fuccia, Andrea Di; Molinari, Cristiano; Rizzo, Anna Paola

doi:10.1016/j.image.2017.04.009

Cited by 30 publications

(17 citation statements)

References 11 publications

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“…Smartphones are now commonly used to record video with HD quality and have Internet access for sharing on the social clouds such as Facebook, Twitter, Tumblr, and Google Plus [1]. Users upload and share HD videos on social clouds but social clouds compress video files for fast loading of the web page, save storage because of a large amount of video data, and provide QoS delivery of video streaming over low-bandwidth networks [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Quality of Experience Assessment of Video Quality in Social Clouds

Laghari

Karim

et al. 2017

Wireless Communications and Mobile Computing

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Video sharing on social clouds is popular among the users around the world. High-Definition (HD) videos have big file size so the storing in cloud storage and streaming of videos with high quality from cloud to the client are a big problem for service providers. Social clouds compress the videos to save storage and stream over slow networks to provide quality of service (QoS). Compression of video decreases the quality compared to original video and parameters are changed during the online play as well as after download. Degradation of video quality due to compression decreases the quality of experience (QoE) level of end users. To assess the QoE of video compression, we conducted subjective (QoE) experiments by uploading, sharing, and playing videos from social clouds. Three popular social clouds, Facebook, Tumblr, and Twitter, were selected to upload and play videos online for users. The QoE was recorded by using questionnaire given to users to provide their experience about the video quality they perceive. Results show that Facebook and Twitter compressed HD videos more as compared to other clouds. However, Facebook gives a better quality of compressed videos compared to Twitter. Therefore, users assigned low ratings for Twitter for online video quality compared to Tumblr that provided high-quality online play of videos with less compression.

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Section: Introductionmentioning

confidence: 99%

Quality of Experience Assessment of Video Quality in Social Clouds

Laghari

Karim

et al. 2017

Wireless Communications and Mobile Computing

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“…In [16], Li et al proposed an algorithm for extracting photo response nonuniformity (PRNU) noise from video files taken by mobile phone camera. Amerini et al introduced a source identification method for video files shared on social networks, such as Twitter, Facebook, Wechat, etc., the proposed method can extract a composite fingerprint for the mobile phone camera using PRNU [17].…”

Section: Related Workmentioning

confidence: 99%

Video-Based Evidence Analysis and Extraction in Digital Forensic Investigation

Xiao

2019

IEEE Access

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As a result of the popularity of smart mobile devices and the low cost of surveillance systems, visual data are increasingly being used in digital forensic investigation. Digital videos have been widely used as key evidence sources in evidence identification, analysis, presentation, and report. The main goal of this paper is to develop advanced forensic video analysis techniques to assist the forensic investigation. We first propose a forensic video analysis framework that employs an efficient video/image enhancing algorithm for the low quality of footage analysis. An adaptive video enhancement algorithm based on contrast limited adaptive histogram equalization (CLAHE) is introduced to improve the closed-circuit television (CCTV) footage quality for the use of digital forensic investigation. To assist the video-based forensic analysis, a deeplearning-based object detection and tracking algorithm are proposed that can detect and identify potential suspects and tools from footages. INDEX TERMS Forensics investigation, forensic video analysis, video/image enhancement, object detection, deep learning.

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“…Modern cameras, particularly those installed in smartphones, go to great lengths to produce visually appealing imagery, and techniques such as lens distortion correction, electronic image stabilization, or high dynamic range imaging have all been found to impede camera identification if not accounted for through spatial resynchronization [5]- [7]. Robustness to strong image and video compression is another major concern [8]- [10] that has been gaining more and more practical relevance due the widespread sharing of visual media through online social networks [11]. Finally, the ability to WIFS'2019, December, [9][10][11][12]2019, Delft, Netherlands.…”

Section: Introductionmentioning

confidence: 99%

“…Robustness to strong image and video compression is another major concern [8]- [10] that has been gaining more and more practical relevance due the widespread sharing of visual media through online social networks [11]. Finally, the ability to WIFS'2019, December, [9][10][11][12]2019, Delft, Netherlands. Approved for public release: distribution unlimited.…”

Section: Introductionmentioning

confidence: 99%

SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learning

Kirchner

Johnson

2019

2019 IEEE International Workshop on Information Forensics and Security (WIFS)

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We explore means to advance source camera identification based on sensor noise in a data-driven framework. Our focus is on improving the sensor pattern noise (SPN) extraction from a single image at test time. Where existing works suppress nuisance content with denoising filters that are largely agnostic to the specific SPN signal of interest, we demonstrate that a deep learning approach can yield a more suitable extractor that leads to improved source attribution. A series of extensive experiments on various public datasets confirms the feasibility of our approach and its applicability to image manipulation localization and video source attribution. A critical discussion of potential pitfalls completes the text.

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Dealing with video source identification in social networks

Cited by 30 publications

References 11 publications

Quality of Experience Assessment of Video Quality in Social Clouds

Quality of Experience Assessment of Video Quality in Social Clouds

Video-Based Evidence Analysis and Extraction in Digital Forensic Investigation

SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learning

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