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A New Dataset for Source Identification of High Dynamic Range Images

Hybrid reference-based Video Source Identification

Department of Information Engineering, University of Florence, 50139 Florence, Italy
FORLAB—Multimedia Forensics Laboratory, PIN Scrl, 59100 Prato, Italy
Amped Software, Loc. Padriciano 99, 34149 Trieste, Italy
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 649;
Received: 12 October 2018 / Revised: 28 January 2019 / Accepted: 1 February 2019 / Published: 5 February 2019
(This article belongs to the Special Issue Camera Identification on Mobile Devices)
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. View Full-Text
Keywords: image forensics; video forensics; social media; sensor pattern noise; smartphone; video database image forensics; video forensics; social media; sensor pattern noise; smartphone; video database
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MDPI and ACS Style

Iuliani, M.; Fontani, M.; Shullani, D.; Piva, A. Hybrid reference-based Video Source Identification. Sensors 2019, 19, 649.

AMA Style

Iuliani M, Fontani M, Shullani D, Piva A. Hybrid reference-based Video Source Identification. Sensors. 2019; 19(3):649.

Chicago/Turabian Style

Iuliani, Massimo, Marco Fontani, Dasara Shullani, and Alessandro Piva. 2019. "Hybrid reference-based Video Source Identification" Sensors 19, no. 3: 649.

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