TUB-IRML at MediaEval 2014 Visual Privacy Task: Privacy Filtering through Blurring and Color Remapping

Competence Center Information Retrieval & Machine Learning

TUB-IRML at MediaEval 2014 Visual Privacy Task: Privacy Filtering through Blurring and Color Remapping

Dominique Maniry, Esra Acar, Sahin Albayrak

Outline

217 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task

►The Privacy Filter

►Sample Outputs of the Filter

►Discussion on the Filter

►Performance Evaluation

►Conclusions & Future Work

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.

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

Step 1: Blur


Step 2: Reduce Colors


Step 2: Remap Colors


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.

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.

Step 4: Include Shape Information (2)


A walking person Two people fighting

Sample Outputs of the Filter (1)


Sample Outputs of the Filter (2)


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).

Simple.

Identity related details can leak through shape.

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.

Performance Evaluation (2)


Stream 1 Results Stream 2 Results

Stream 3 Results

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.

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

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Sekretariat TEL 14

Ernst-Reuter-Platz 7

10587 Berlin, Deutschland

17

Esra Acar

Researcher

M.Sc.

[email protected]

Thanks!

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TUB-IRML at MediaEval 2014 Visual Privacy Task17 October 2014

http://www.dai-labor.de/

Software

TUB-IRML at MediaEval 2014 Visual Privacy Task: Privacy Filtering through Blurring and Color Remapping