The document describes a privacy filter developed by TUB-IRML to obscure identity-related regions in images. The filter blurs and remaps colors in privacy regions while preserving some shape information with edges. It includes four steps: blurring privacy regions, reducing colors and remapping them, applying a blending mask, and including edges. Sample outputs and a discussion of pros and cons are presented. Performance was evaluated in three streams of participants. The conclusion states that the filter is effective at privacy protection while balancing intelligibility, and future work will refine parameters and use segmentation.

1. TUB-IRML at MediaEval 2014 Visual Privacy Task:
Privacy Filtering through Blurring and Color Remapping
Dominique Maniry, Esra Acar, Sahin Albayrak
Competence Center Information Retrieval & Machine Learning

2. Outline
►The Privacy Filter
►Sample Outputs of the Filter
►Discussion on the Filter
►Performance Evaluation
►Conclusions & Future Work
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 2

3. The Privacy Filter (1)
►Main idea: To obscure both shape and appearance of
identity-related regions through blurring and color
remapping.
►Preserve the intelligibility by
displaying edges, and
hinting anomalous events through special colors.
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 3

4. The Privacy Filter (2)
►The filter contains four steps:
Step 1: Blur all privacy-related regions
Step 2: Reduce number of colors & remap colors
Step 3: Apply a blending mask
Step 4: Include shape information by incorporating
edges
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 4

5. Step 1: Blur
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 5

6. Step 2: Reduce Colors
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 6

7. Step 2: Remap Colors
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 7

8. Step 3: Apply a Blending Mask
►The blending mask mask(x, y) is a binary image where
annotated regions have a value of 1 and remaining
regions have a value 0.
►The smoothing is achieved by applying a Gaussian blur
to the blending mask.
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 8

9. Step 4: Include Shape Information (1)
►The obscured regions are overlaid with edges
obtained with Canny Edge detection.
►Edges in regions with a high privacy requirement (i.e.,
faces) are discarded.
►The remaining edges are emphasized using
morphological dilation with a 3x3 circle as structuring
element.
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 9

10. Step 4: Include Shape Information (2)
A walking person Two people fighting
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 10

11. Sample Outputs of the Filter (1)
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 11

12. Sample Outputs of the Filter (2)
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 12

13. Discussion on the Filter
Pros Cons
 Parameters to tune trade-off
between privacy and intelligibility
(blur intensity and number of
colors).
 Remapped colors can convey
additional information.
 Different regions can have
different privacy levels by using
different blur intensities (e.g.,
face more blurred than full
body).
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 13
 Simple.
 Identity related details can leak
through shape.

14. Performance Evaluation (1)
Stream 1: 230 crowd-sourcing workers.
Stream 2: 65 people working at Thales (mainly in R&D).
Stream 3: 59 participants from sectors including R&D, data protection and
law enforcement from all around the world.
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 14

15. Performance Evaluation (2)
Stream 1 Results Stream 2 Results
Stream 3 Results
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 15

16. Conclusions & Future Work
►The user study has shown that our method is very
effective at protecting privacy.
►Future work
Evaluating different parameters to balance privacy and
intelligibility, and
 Improving the appropriateness by reducing the obscured
regions using a pixel-wise segmentation.
17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 16

17. M.Sc.
Competence Center Information Retrieval &
Machine Learning
www.dai-labor.de
Fon
Fax
+49 (0) 30 / 314 – 74
+49 (0) 30 / 314 – 74 003
DAI-Labor
Technische Universität Berlin
Fakultät IV – Elektrontechnik & Informatik
Sekretariat TEL 14
Ernst-Reuter-Platz 7
10587 Berlin, Deutschland
17
Esra Acar
Researcher
esra.acar@tu-berlin.de
Thanks!
013
TUB-IRML at 17 October 2014 MediaEval 2014 Visual Privacy Task