Hybrid reference-based Video Source Identification
Abstract
:1. Introduction
2. Digital Video Source Device Identification Based on Sensor Pattern Noise
3. Hybrid Sensor Pattern Noise Analysis
- the resize and cropping factors are determined by the device model (and, possibly, firmware). It is thus possible to build a lookup table that eliminates the need for an exhaustive search when information about the reference model is available;
- even when no information about the model is available, it is not necessary to repeat the whole search on all frames. Once a sufficiently high correlation is found for a given scale, the search can be restricted around it.
3.1. Source Identification of Digitally Stabilized Videos
3.2. Hybrid Reference-Based Video Source Identification Pipeline
3.3. Extension to Contents Shared on Social Media Platforms
3.4. Extension to Digital Zoom
4. Dataset for HSI
- On the reference side: 100 flat-field images depicting skies or walls; 150 images of indoor and outdoor scenes; 1 video of the sky captured with slow camera movement, longer than 10 s;
- On the query side: videos of flat surfaces, indoor scenes and outdoor scenes. For each of the video categories (flat, indoor and outdoor) at least 3 different videos have been captured considering the three different scenarios available in the Dataset: (i) still camera, (ii) walking operator and (iii) panning and rotating camera. We will refer to them as still, move and panrot videos respectively. Thus, each device has at least 9 videos, each one lasting more than 60 s.
5. Experimental Validation
- We determine the cropping and scaling parameters applied by each device model in the considered set;
- We verify that, in the case of non-stabilized video, the performance of the hybrid approach is comparable with the source identification based on a video reference;
- We show the effectiveness in identifying the source of in-camera digitally stabilized videos;
- We show the performance in linking Facebook and YouTube profiles;
- We demonstrate the effectiveness of the method in the presence of digital zoom.
5.1. Fingerprints Matching Parameters
5.2. Hybrid Reference-Based Video Source Identification Performance
5.3. Hybrid Reference-Based Video Source Identification Performance on Stabilized Videos
5.4. Results on Contents from SMPs
5.5. Results on Digitally Zoomed Videos
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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ID | Model | Image Resolution | Video Resolution | Digital Stab |
---|---|---|---|---|
C1 | Galaxy S3 | off | ||
C2 | Galaxy S3 Mini | off | ||
C3 | Galaxy S3 Mini | off | ||
C4 | Galaxy S4 Mini | off | ||
C5 | Galaxy Tab 3 10.1 | off | ||
C6 | Galaxy Tab A 10.1 | off | ||
C7 | Galaxy Trend Plus | off | ||
C8 | Ascend G6 | off | ||
C9 | Ipad 2 | off | ||
C10 | Ipad Mini | on | ||
C11 | Iphone 4s | on | ||
C12 | Iphone 5 | on | ||
C13 | Iphone 5c | on | ||
C14 | Iphone 5c | on | ||
C15 | Iphone 6 | on | ||
C16 | Iphone 6 | on | ||
C17 | Lumia 640 | off | ||
C18 | Xperia Z1c | on |
ID | Scaling | Central Crop along x and y axes |
---|---|---|
C1 | 0.59 | [0 307] |
C2 | 0.5 | [0 228] |
C3 | 0.5 | [0 228] |
C4 | 0.59 | [0 0] |
C5 | 1 | [408 354] |
C6 | 0.49 | [0 246] |
C7 | 0.5 | [0 240] |
C8 | 0.39 | [0 306] |
C9 | 1 | [−160 0] |
C17 | 0.59 | [0 1] |
ID | Scaling | Central Crop along x and y | Rotation (CCW) |
---|---|---|---|
C10 | [0.806 0.815 0.821] | [243 256 261] [86 100 103] | [−0.2 0 0.2] |
C11 | [0.748 0.750 0.753] | [380 388 392] [250 258 265] | [−0.2 0 0.2] |
C12 | [0.684 0.689 0.691] | [287 294 304] [135 147 165] | [−0.2 0 0.6] |
C13 | [0.681 0.686 0.691] | [301 318 327] [160 181 195] | [−0.4 0 1] |
C14 | [0.686 0.686 0.689] | [261 301 304] [119 161 165] | [−0.4 0 0] |
C15 | [0.696 0.703 0.713] | [298 322 345] [172 190 218] | [−0.2 0.2 1.6] |
C16 | [0.703 0.706 0.708] | [315 323 333] [178 187 201] | [−0.2 0.2 0.4] |
C18 | [0.381 0.384 0.387] | [548 562 574] [116 121 126] | [0 0 0] |
Reference | Query | TPR [10] | FPR [10] |
---|---|---|---|
Non-stabilized | Stabilized | 0.83 | 0 |
Stabilized | Stabilized | 0.65 | 0 |
Aggregation Threshold () | Accuracy | TPR | FPR |
---|---|---|---|
30 | 89% | 0.79 | 0.02 |
32 | 89% | 0.82 | 0.05 |
34 | 90% | 0.84 | 0.03 |
36 | 93% | 0.87 | 0.01 |
38 | 93% | 0.86 | 0 |
40 | 93% | 0.87 | 0.01 |
42 | 93% | 0.85 | 0 |
44 | 93% | 0.85 | 0 |
46 | 93% | 0.85 | 0 |
48 | 92% | 0.84 | 0 |
50 | 92% | 0.83 | 0 |
52 | 91% | 0.82 | 0 |
54 | 91% | 0.82 | 0 |
Test Case | Max | Scale |
---|---|---|
Zoom only (1080p video) | 1.3085 | |
Zoom and YouTube @1080p | 1.3085 | |
Zoom and YouTube @720p | 0.8722 |
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Iuliani, M.; Fontani, M.; Shullani, D.; Piva, A. Hybrid reference-based Video Source Identification. Sensors 2019, 19, 649. https://doi.org/10.3390/s19030649
Iuliani M, Fontani M, Shullani D, Piva A. Hybrid reference-based Video Source Identification. Sensors. 2019; 19(3):649. https://doi.org/10.3390/s19030649
Chicago/Turabian StyleIuliani, Massimo, Marco Fontani, Dasara Shullani, and Alessandro Piva. 2019. "Hybrid reference-based Video Source Identification" Sensors 19, no. 3: 649. https://doi.org/10.3390/s19030649
APA StyleIuliani, M., Fontani, M., Shullani, D., & Piva, A. (2019). Hybrid reference-based Video Source Identification. Sensors, 19(3), 649. https://doi.org/10.3390/s19030649