This paper introduces a new privacy filter adopted in the context of the DPT (Drone Protect Task) at MediaEval Benchmark 2015. Our proposed filter protects privacy by visually replacing sensitive RofI (Regions of Interest) by its shapes. A combination of steganography and scrambling is used in order to make this filter. Once the scrambling is applied on the pixels of the RofI, its MSB (Most Significant Bit) are hidden in the LSB (Least Significant Bit) of a cover image. Our filter fulfils four criteria defined by DPT: near-lossless reversibility, intelligibility, appropriateness and anonymization. We benchmarked the filter on the last three criteria and we get good results: 40 % for intelligibility and appropriateness, and 60 % for anonymization. http://ceur-ws.org/Vol-1436/ http://www.multimediaeval.org

1. Privacy Protection Filter
using StegoScrambling
in Video Surveillance
Natacha Ruchaud and Jean-Luc Dugelay
Speaker : Natacha Ruchaud

2. Privacy Filters in Video
Surveillance
PRIVACY
PLEASENTNESS
INTELLIGIBILITY
2/15
Main objectives :
No more possibility to recognize people
Trade-of
Good looking
Visual quality, still recognize events: people
walking, running, fighting, stealing….

3. Privacy Filters in Video
Surveillance
Time
consuming
Reversibility
Flexibility
3/15
But also :
Videos surveillance in real time
Re identification in case of trouble
No need to adapt parameters

4. Main idea
●Scrambling to protect data
●Steganography to hide the protected
data in an edge map which preserves
global shape and boundary appearance
●Near lossless Reversibility
4/15

5. Main idea
5/15
Edge mapScrambled
RoI
Original
RoI
StegoScrambled
RoI
Crop ConvexHull

6. Scrambling
6/15
Pixel can be represented in 8 bits
Remove the 2 Least significant bits of
the RoI
An XOR between the 6 bits of the RoI
and the random numbers generated with
a pseudo-random number controlled by
a seed.

7. Edge map
7/15

8. Edge map
8/15
DilatationKmeans ClusteringOriginal
RoI
Edges detection AND

9. Steganography
9/15
The 6 bits of the XOR image (pixels intensity is either 0 or 63) are
shifted in the LSBs
The 2 MSBs of the edge map (pixels intensity is either 0 or 192) are
added in the 2 MSBs of the XOR image

10. Recovered image
10/15

11. Flowchart
11/15

12. Results
12/15

13. Results
13/15

14. Future works & Perspectives
Less favorable outcome for intelligibility and pleasantness
●Edges detector and Kmeans clustering distortions
●Black and white results
Future works:
●Improve edges detection
●New edge map
14/15

15. THANK YOU
15/15