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Reversible visual privacy protection

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Reversible visual privacy protection

  1. 1. 1 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Reversible visual privacy protection Touradj Ebrahimi touradj.ebrahimi@epfl.ch COST IC1206 Training School De-identification for privacy protection in multimedia content 7-11 October 2015, Limassol, Cyprus
  2. 2. 2 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Outline • Part I: – Motivations and context – Conventional privacy protection filters – Advanced privacy protection filters • Part II: – Visual privacy evaluation framework – Impact of new imaging modalities on privacy – Illustrative example
  3. 3. 3 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Motivation • People are increasingly exposed: – video surveillance – social media
  4. 4. 4 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Proliferation of video surveillance applications • Surveillance of sensitive locations – Embassies, airports, nuclear plants, military zone, border control, … • Intrusion detection – Residential surveillance, retail surveillance, … • Traffic control – Speed control • Access to places – Car license plate recognition in cities • Event detection – Child/Elderly care • Marketing/statistics – Customers habits – Number of visitors • …
  5. 5. 5 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Proliferation of social media applications
  6. 6. 6 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Main security solutions for visual content protection • Encryption – Secure communication – Conditional access • Integrity verification – Digital signature – Proof for lack of manipulation after capture
  7. 7. 7 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Alternatives to implement video surveillance with privacy • Fully automatic surveillance without intervention of human operators – False positives and false negatives • Encrypting the whole video – Not good for monitoring • Distorting/blocking sensitive regions – Impact on intelligibility • Reversible encryption/scrambling of sensitive regions with a key – Identification can take place when crime happens • Legal and best practices in video surveillance – Recorded materials locked in secure locations • Only extract/record needed information from the scene – MPEG-7 visual descriptors
  8. 8. 8 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Privacy-sensitive visual information • Predefined zones – Windows, doors – Bank teller – Casino playing tables – … • Automatic identification of Regions of Interest (ROI) – People in the scene – Human faces – Cars license plates – Moving objects – … • Advanced image/video analytics – Deep learning – Big data analytics
  9. 9. 9 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Social media/networks business model • User profiling • Targeted advertisement/marketing
  10. 10. 10 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Requirements for visual privacy protection • Maximize intelligibility • Minimize invasion of privacy • Visually pleasing • Reversible • Reasonable computational resources • Format preserving/independent • Reliable • Secure • …
  11. 11. 11 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Legacy solutions to visual privacy protection • Masking • Blur • Pixelization
  12. 12. 12 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Masking
  13. 13. 13 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Blur
  14. 14. 14 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Pixelization
  15. 15. 15 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne More recent solutions for privacy protection • (ROI) Encryption • (ROI) Scrambling
  16. 16. 16 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne ROI selective encryption
  17. 17. 17 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne ROI selective decryption
  18. 18. 18 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne ROI selective scrambling
  19. 19. 19 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Bitstream encryption • Selective encryption of the bitstream at packet level • One or more secret keys • Symmetric encryption – Packet body – Block cipher: e.g. AES packet encrypted packet private key
  20. 20. 20 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling approaches • Image-domain – Randomly flip bits in one or more bit planes • Pros – Very simple – Independent from the subsequent encoding scheme – Does not affect the bitstream syntax → standard compliance • Cons – Significantly alter statistics of video signal – Ensuing compression less efficient bitstreamimage Scrambling Encoder Transform Entropy Coding
  21. 21. 21 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling approaches • Transform-domain – Randomly flip sign of transform coefficients • Pros – Does not adversely affect subsequent entropy coding – Strength of scrambling can be controlled – Does not affect the bitstream syntax → standard compliance • Cons – Must be integrated inside the encoder bitstreamimage Transform Encoder Scrambling Entropy Coding
  22. 22. 22 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling approaches • Bitstream-domain – Randomly flip bits in bitstream • Pros – Applied on bitstream after encoding • Cons – Require parsing of bitstream – Difficult to guarantee syntax remains compliant and will not crash a decoder bitstreamimage Encoder Transform Entropy Coding Scrambling
  23. 23. 23 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in JPEG (a) (b) DC PRNG pseudo-randomly inverse sign scrambled codestream public key assymetric encryption seed (c) DC PRNG pseudo-randomly inverse sign scrambled codestream public key assymetric encryption seed (d) DC PRNG pseudo-randomly inverse sign scrambled codestream public key assymetric encryption seed Figure 4 – AC coefficients scrambling: (a) 63 AC coefficients, (b) 60 AC coefficients, (c) 55 AC coefficients, (d) 48 AC coefficients. Straightforwardly, as the scrambling is merely flipping signs of selected coefficients, the technique requires negligible computational complexity. Clearly, the shape of the scrambled region is restricted to match the 8x8 DCT blocks boundaries.
  24. 24. 24 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in JPEG (c) (d)
  25. 25. 25 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in JPEG 2000 (JPSEC) • Codeblock-based bitstream domain scrambling    ≥ <+= → 900 900900mod)(' xxifx xxifxmxx x Preserve the markers in the bitstream; do not introduce erroneous markers x=current byte, y=preceding byte 1. If x=0xFF, no modification 2. If y=0xFF 3. Otherwise where m is an 8-bit pseudo- random number in [0x00,0x8F] where n is an 8-bit pseudo- random number in [0x00,0xFE] xFFnxxx 0mod)(' +=→ selective scrambling PRNG seed encryption encrypted seed scrambled codestream JPSEC codestream JPSEC syntax codestream quantizer selective scrambling PRNG seed wavelet transform arithmetic coder encryption encrypted seed scrambled codestream JPSEC codestream JPSEC syntax image Encoder Decoder
  26. 26. 26 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in JPEG 2000 (JPSEC) • ROI-based wavelet domain scrambling – Arbitrary-shape regions • Exploit ROI mechanisms in JPEG 2000 Encoder Decoder quantizerwavelet transform arithmetic coder segmentation mask ? image down-shift wavelet coefficient PRNG seeds encrypt seeds ROI-based scrambled JPSEC code-stream scramble wavelet coefficient resolution level l < TI ? up-scale code-block distortion foreground objects background keys resolution level l ≥ TS ? yes no yesno inverse quantizer inv. wavelet transform arithmetic decoder coefficient < 2 s ? image up-shift wavelet coefficient PRNG seeds decrypt seeds ROI-based scrambled JPSEC code-stream unscramble wavelet coefficient foreground objects background keys resolution level l ≥ TS ? yes no Decoder
  27. 27. 27 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in JPEG 2000
  28. 28. 28 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in MPEG-4
  29. 29. 29 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in MPEG-4
  30. 30. 30 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in MPEG-4
  31. 31. 31 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in H.264/AVC
  32. 32. 32 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in H.264/AVC
  33. 33. 33 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne An existing product Scrambler Unscrambler
  34. 34. 34 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in DVCScrambling in DVC • Key frame privacy (JPEG) – Scrambling in the transform domain on the DCT coefficients. – Driven by a Pseudo-Random Number Generator (PRNG) to pseudo- randomly invert the sign of the DCT Coefficients. • WZ frames DCT scrambler DVC scheme with privacy protection
  35. 35. 35 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Scrambling in DVCScrambling in DVC a) Key frame (JPEG). b) Wyner-Ziv transform domain scrambling.
  36. 36. 36 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne MPEG-7 camera XML scene description The MPEG-7 camera describes a scene in terms of semantic objects and of their properties
  37. 37. 37 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne MPEG-7 camera – Image analysis: segmentation, change detection, and tracking (implemented on the camera DSP). – MPEG-7 coder: scene description represented using MPEG-7 (XML). – MPEG-7 decoder: MPEG-7 description is parsed. Extraction of the information related to the specific applications.
  38. 38. 38 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne MPEG-7 camera <!-- ################################################## --!> <!-- DDL output for object 4 --!> <!-- ################################################## --!> <Object id="4"> <RegionLocator> <BoxPoly> Poly </BoxPoly> <Coords1> 237 222 </Coords1> <Coords2> 230 252 </Coords2> <Coords3> 240 286 </Coords3> <Coords4> 308 287 </Coords4> <Coords5> 312 284 </Coords5> </RegionLocator> <DominantColor> <ColorSpace> YUV </ColorSpace> <ColorValue1> 143.4 </ColorValue1> <ColorValue2> 123.3 </ColorValue2> <ColorValue3> 128.2 </ColorValue3> </DominantColor> <HomogeneousTexture> <TextureValue> 9.02 </TextureValue> </HomogeneousTexture> <MotionTrajectory> <TemporalInterpolation> <KeyFrame> 100 </KeyFrame> <KeyPos> 268.6 251.7 </KeyPos> <KeyFrame> 101 </KeyFrame> <KeyPos> 262.8 241.0 </KeyPos> ... <KeyFrame> 138 </KeyFrame> <KeyPos> 192.9 79.0 </KeyPos> </TemporalInterpolation> </MotionTrajectory> </Object> XML scene description
  39. 39. 39 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne MPEG-7 camera for video surveillance original frame segmentation mask bounding box
  40. 40. 40 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Existing product
  41. 41. 41 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Warping-based privacy filter • Compute map between original and shifted points • Interpolate in-between pixels with ‘cubic’ original points shifted points transformation
  42. 42. 42 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Recovery from warping • Know original points and seed for random algorithm to compute shifted points • Perform reverse mapping and interpolation shifted points original points transformation
  43. 43. 43 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne mild medium strong Facial features-based warping
  44. 44. 44 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne mild medium strong Unwarping
  45. 45. 45 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Morphing
  46. 46. 46 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Morphing
  47. 47. 47 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Privacy filter with false color
  48. 48. 48 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne JPEG Transmorphing JPEG Transcoder Mask matrix Sub-image Morphed JPEG image Original image Processed image − Sub-image embedded in APPn Markers T Reconstructed image 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 1 ! 1 ! 0 ! 0 ! 0 ! 0 ! 1 ! 1 ! 0 ! 1 ! 1 ! 1 ! 1 ! 0 ! 0 ! 1 ! 1 ! 1 ! 1 ! 1 ! 1 ! 1 ! 1 ! 0 ! 0 ! 1 ! 1 ! 1 ! 1 ! 0 ! 1 ! 1 ! 0 ! 0 ! 0 ! 0 ! 1 ! 1 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! 0 ! JPEG Transcoder Threshold t JPEG Transcoder
  49. 49. 49 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne JPEG Security and Privacy SOI APP1 (Exif) EOI SOI APP1 (Exif) EOI APP11 (protected metadata) JPEG-1 decoder JPEG Privacy & Security decoder APP1 (Exif) APP1 (Exif) original JPEG codestream JPEG compatible codestream with data protection Image Data Image data APP11 (protected image data) Image Data APP11 (protected metadata) Image data APP11 (protected image data) APP3 (JPSearch) APP3 (JPSearch) APP3 (JPSearch)
  50. 50. 50 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Homework! • Any non-reversible privacy protection filter can be converted into a reversible version! – Propose how this can be done!
  51. 51. 51 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Thanks for your attention! End of Part I
  52. 52. 52 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Outline • Part I: – Motivations and context – Conventional privacy protection filters – Advanced privacy protection filters • Part II: – Visual privacy evaluation framework – Impact of new imaging modalities on privacy – Illustrative example
  53. 53. 53 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Visual privacy evaluation framework
  54. 54. 54 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Privacy-intelligibility tradeoff • Protect privacy – obfuscate or remove personal information from the video • Perform surveillance – determine suspicious event/person, apprehend and prosecute criminals • Where is the balance?
  55. 55. 55 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Evaluation tools • Subjective evaluation • Crowdsourcing • Objective metrics
  56. 56. 56 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Subjective evaluation • Three naïve filters: blurring, pixelization, and masking • Dataset of 8 annotated videos – People acting normally or abnormally – Wearing glasses, scarf, hats, etc. – Blinking into the camera
  57. 57. 57 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Example
  58. 58. 58 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Questions asked to subjects race white asian I don’t know gender female male I don’t know glasses yes no I don’t know sunglasses yes no I don’t know scarf yes no I don’t know blinking yes no I don’t know Privacy Intelligibility
  59. 59. 59 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Subjective results 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 blurring filter pixelization filter masking filter Privacy
  60. 60. 60 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Facebook-based evaluations • The same experiment as for subjective evaluations • Facebook-based system – Shows videos – Collects answers – Trusted workers
  61. 61. 61 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Offline results Onlineresults blurring masking pixelization Crowdsourcing: privacy
  62. 62. 62 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Offline results Onlineresults blurring masking pixelization Crowdsourcing: intelligibility
  63. 63. 63 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne • The cheapest and the most scalable option • People counting in public transport – Face recognition is the metric of privacy – Face detection is the metric of intelligibility • An ideal privacy protection filter – Degrades face recognition – Does not affect face detection Objective evaluations
  64. 64. 64 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne • Increase strength of privacy filter • Note relative decrease in accuracy of face detection and recognition Evaluation examples
  65. 65. 65 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Detection Recognition Gaussian kernel size Accuracy Blurring, FERET dataset
  66. 66. 66 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Morphing, FERET dataset 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Accuracy Intensity strength interpolation_0 interpolation_0.2 interpolation_0.4 interpolation_0.6 interpolation_0.8 interpolation_1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Accuracy Intensity strength interpolation_0 interpolation_0.2 interpolation_0.4 interpolation_0.6 interpolation_0.8 interpolation_1 Recognition morphed Recognition recovered
  67. 67. 67 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Impact of new imaging modalities on privacy
  68. 68. 68 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne New imaging modalities • Ultra high definition (UHD)
  69. 69. 69 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne New imaging modalities • High dynamic range (HDR)
  70. 70. 70 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne New imaging modalities • Video from mini-drones
  71. 71. 71 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Ultra High Definition (UHD)
  72. 72. 72 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Walking example
  73. 73. 73 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Exchanging bag example
  74. 74. 74 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Fighting example
  75. 75. 75 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Subjective evaluations of privacy • 4K UHD Sony reference monitor • Evaluation questions about – People’s accessories – Main action – Visible items – Gender – Race • Accompanied questions on certainty
  76. 76. 76 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Measuring privacy %ofcorrectanswers
  77. 77. 77 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne High Dynamic Range (HDR)
  78. 78. 78 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Woman Elderly Wears sunglasses Woman?? Woman Young?? Woman Young?? Man?? Middle Age??Man?? Middle Age?? Man Middle Aged Man Middle Aged Woman Young Woman Young • HDR surveillance cameras • More details in the scene • More privacy intrusive? • HDR monitor is needed • Tone-mapped images Implications of HDR
  79. 79. 79 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  80. 80. 80 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  81. 81. 81 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  82. 82. 82 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne
  83. 83. 83 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Crowdsourcing evaluations of images • About 400 people participated • Evaluation questions about – Gender – Race – Age – Color of clothes – How many people
  84. 84. 84 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Crowdsourcing results
  85. 85. 85 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne (Mini-)drone surveillance
  86. 86. 86 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Drones & surveillance • Can collect sensitive data – Different heights – Different angles – Fly over fences • Bird-fly view • Harass and follow targets • Privacy protection?
  87. 87. 87 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Privacy protection filters • Blurring • Pixelization • Masking • Warping • Morphing
  88. 88. 88 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Privacy protection filters • Different strength levels – Mild – Noticeable – Obfuscating – Completely obfuscating
  89. 89. 89 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Crowdsourcing evaluations • Seven selected video contents – 5 privacy filters at 4 levels of strength + original = 21 versions of each content • 850 online workers from around the world (Microworkers platform) – Worker accesses one version of the content • Six questions about personal privacy and intelligibility of surveillance – Also, asked how certain is the answer
  90. 90. 90 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Evaluation framework • Questions – Activity in the scene – Number of people – Items (camera, wallet, etc.) – Accessories people wear (jacket, hat, helmet, sunglasses, etc.) – Gender – Ethnicity
  91. 91. 91 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Results – correctness Pixelization
  92. 92. 92 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Results – correctness Morphing
  93. 93. 93 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Results – certainty Pixelization
  94. 94. 94 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Results – certainty Morphing
  95. 95. 95 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Privacy-intelligibility tradeoff
  96. 96. 96 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne ProShare: A privacy-aware photo sharing platform
  97. 97. 97 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Illustrative example User 1 Client-side Notify User 2 Server-side Protect User 2 User 2 User 3 User 1User 2 Protect User 1 Friend relationship: User 1 & 2: ✔ User 1 & 3: ✔ User 2 & 3: ✖ Social Networking Services URL • Sender-side operations – Protection and upload • Server-side operations – Hosting and Access control • Recipient-side operations – Download and Reconstruction
  98. 98. 98 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne JPEG Privacy & Security
  99. 99. 99 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne JPEG Security and Privacy SOI APP1 (Exif) EOI SOI APP1 (Exif) EOI APP11 (protected metadata) JPEG-1 decoder JPEG Privacy & Security decoder APP1 (Exif) APP1 (Exif) original JPEG codestream JPEG compatible codestream with data protection Image Data Image data APP11 (protected image data) Image Data APP11 (protected metadata) Image data APP11 (protected image data) APP3 (JPSearch) APP3 (JPSearch) APP3 (JPSearch)
  100. 100. 100 Multimedia Signal Processing Group Swiss Federal Institute of Technology, Lausanne Thanks for your attention! End of Part II

Editor's Notes

  • We first checked FERET dataset as being the most popular for face recognition. On the graphs, we see how accuracy of face detection (or recognition on the right) are changing when we increase the gaussian kernel of the blurring filter. Detection is not affected by the blurring but recognition is, especially recognition of LBP (local binary pattern) based algorithm
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