1. 1. TUB-IRML at MediaEval 2014 Visual Privacy Task:Privacy Filtering through Blurring and Color RemappingDominique Maniry, Esra Acar, Sahin AlbayrakCompetence Center Information Retrieval & Machine Learning
2. 2. OutlineThe Privacy FilterSample Outputs of the FilterDiscussion on the FilterPerformance EvaluationConclusions & Future Work17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 2
3. 3. The Privacy Filter (1)Main idea: To obscure both shape and appearance ofidentity-related regions through blurring and colorremapping.Preserve the intelligibility bydisplaying edges, andhinting anomalous events through special colors.17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 3
4. 4. The Privacy Filter (2)The filter contains four steps:Step 1: Blur all privacy-related regionsStep 2: Reduce number of colors & remap colorsStep 3: Apply a blending maskStep 4: Include shape information by incorporatingedges17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 4
5. 5. Step 1: Blur17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 5
6. 6. Step 2: Reduce Colors17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 6
7. 7. Step 2: Remap Colors17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 7
8. 8. Step 3: Apply a Blending MaskThe blending mask mask(x, y) is a binary image whereannotated regions have a value of 1 and remainingregions have a value 0.The smoothing is achieved by applying a Gaussian blurto the blending mask.17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 8
9. 9. Step 4: Include Shape Information (1)The obscured regions are overlaid with edgesobtained with Canny Edge detection.Edges in regions with a high privacy requirement (i.e.,faces) are discarded.The remaining edges are emphasized usingmorphological dilation with a 3x3 circle as structuringelement.17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 9
10. 10. Step 4: Include Shape Information (2)A walking person Two people fighting17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 10
11. 11. Sample Outputs of the Filter (1)17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 11
12. 12. Sample Outputs of the Filter (2)17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 12
13. 13. Discussion on the FilterPros Cons Parameters to tune trade-offbetween privacy and intelligibility(blur intensity and number ofcolors). Remapped colors can conveyadditional information. Different regions can havedifferent privacy levels by usingdifferent blur intensities (e.g.,face more blurred than fullbody).17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 13 Simple. Identity related details can leakthrough shape.
14. 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 andlaw enforcement from all around the world.17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 14
15. 15. Performance Evaluation (2)Stream 1 Results Stream 2 ResultsStream 3 Results17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 15
16. 16. Conclusions & Future WorkThe user study has shown that our method is veryeffective at protecting privacy.Future workEvaluating different parameters to balance privacy andintelligibility, and Improving the appropriateness by reducing the obscuredregions using a pixel-wise segmentation.17 October 2014 TUB-IRML at MediaEval 2014 Visual Privacy Task 16
17. 17. M.Sc.Competence Center Information Retrieval &Machine Learningwww.dai-labor.deFonFax+49 (0) 30 / 314 74+49 (0) 30 / 314 74 003DAI-LaborTechnische Universitt BerlinFakultt IV Elektrontechnik & InformatikSekretariat TEL 14Ernst-Reuter-Platz 710587 Berlin, Deutschland17Esra AcarResearcheresra.acar@tu-berlin.deThanks!013TUB-IRML at 17 October 2014 MediaEval 2014 Visual Privacy Task