Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Images to Privacy Leakage

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Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Images to Privacy Leakage. Presentation given at the 10th International Workshop on Digital Forensics and Watermarking (IWDW'11).

Note that a more extensive objective and subjective study of privacy protection in video surveillance systems can be found in the following book chapter:

H. Sohn, D. Lee, W. De Neve, K.N. Plataniotis, and Y.M. Ro. An objective and subjective evaluation of content-based privacy protection of face images in video surveillance systems using JPEG XR. Effective Surveillance for Homeland Security: Balancing Technology and Social Issues. CRC Press / Taylor & Francis. May 2013. pp. 111-140.

http://www.citeulike.org/user/wmdeneve/article/10831550
http://www.crcpress.com/product/isbn/9781439883242

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Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Images to Privacy Leakage

  1. 1. Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Images to Privacy Leakage10th International Workshop on Digital Forensics and Watermarking October 2011 Hosik Sohn1, Dohyoung Lee2, Wesley De Neve1, Konstantinos N. Plataniotis2, and Yong Man Ro1 1Korea Advanced Institute of Science and Technology (KAIST), Image and Video Systems Lab 2University of Toronto, Multimedia Lab
  2. 2. -2-Contents1. Introduction2. Layered Scrambling for Motion JPEG XR3. Assessment of Chroma-induced Privacy Leakage 3.1 Objective Assessments 3.2 Subjective Assessments4. Discussion and Conclusions
  3. 3. -3-INTRODUCTION
  4. 4. -4-1. Introduction Present-day video surveillance systems often come with high-speed network connections, plenty of storage capacity, and high processing power The increasing ability of video surveillance systems to identify people has recently raised several privacy concerns To mitigate these privacy concerns, scrambling can be leveraged to conceal the identity of face images in video content originating from surveillance cameras Privacy protected surveillance videos
  5. 5. -5-1. Introduction The past few years have witnessed the development of a wide range of content-based tools for protecting privacy in video surveillance systems  Dependent on the location where scrambling (or encryption) is applied, three different approaches of scrambling can be distinguished  Uncompressed domain scrambling  Transform domain scrambling  Compressed bit stream domain scrambling  One of the main challenges is the concealment of privacy-sensitive regions by making use of invertible transformation of visual information at a low computational cost
  6. 6. -6-1. Introduction Content-based tools for privacy protection need to find a proper balance between the level of security offered and the amount of bit rate overhead  In general, altering the visual information present in privacy-sensitive regions typically breaks the effectiveness of coding tools Coding Security efficiency level To limit the bit rate overhead, many content-based tools for privacy protection only scramble luma information, leaving chroma information unprotected
  7. 7. -7-1. Introduction In this paper, we investigate the contribution of non-scrambled chroma information to privacy leakage To that end, we study and quantify the influence of the presence of non- scrambled chroma information on the effectiveness of automatic and human FR  Objective assessment: we apply automatic FR techniques to face images that have been privacy-protected in the luma domain  Subjective assessment: we investigate whether agreement exists between the judgments of 32 human observers and the output of automatic FR
  8. 8. -8- 1. Introduction  FR vs. perception-based security metrics for assessing the level of privacy  Luminance Similarity Score (LSS), Edge Similarity Score (ESS), and Local Feature-based Visual Security Metric (LFVSM)[1,2]  Note that these metrics are general in nature and are thus not able to take advantage of domain-specific information (e.g., face information)[1] Tong, L., Dai, F., Zhang, Y., Li, J. “Visual security evaluation for video encryption,” in: Proceedings of ACM International Conference onMultimedia, 835–838 (2010)[2] Mao Y., Wu M., "A joint signal processing and cryptographic approach to multimedia encryption," IEEE Transactions on Image Processing,15(7), 2061-2075 (2006)
  9. 9. -9-LAYERED SCRAMBLING FORMOTION JPEG XR
  10. 10. -10- 2. Layered Scrambling for Motion JPEG XR  The video surveillance system studied makes use of Motion JPEG XR to encode surveillance video content  Motion JPEG XR offers a low-complexity solution for the intra coding of high-resolution video content, while at the same time offering quality and spatial scalability provisions  Layered scrambling for JPEG XR [3]  Modified JPEG XR encoder Secret key DC subband Scrambling LBT LBT Q Pred. (RLS) • Adaptive LP subband entropy Adaptive Scrambling coding Q Pred. scan (RP) • Fixed HP subband/Flexbits length Adaptive Scrambling coding Q Pred. scan (RSI)[3] Sohn, H., De Neve, W., Ro, Y.M., “Privacy Protection in Video Surveillance Systems: Analysis of Subband-Adaptive Scrambling in JPEGXR,” IEEE Transactions on Circuits and Systems for Video Technology, 21, 170–177 (2011)
  11. 11. -11-2. Layered Scrambling for Motion JPEG XR  Overview of the layered scrambling technique - Random level shift (RLS) for DC subbands DCcoeff e  DCcoeff  R(L), - Random permutation (RP) for LP subbands LPcoeffi e  LPcoeff j , where i  1,..., C, j  x1 ,..., xC , - Random sign inversion (RSI) for HP subbands  HPcoeff , if r  1 HPcoeff e   ,  HPcoeff , otherwise  N denotes the number of MBs, L denotes the RLS parameter, K denotes the number of non-zero LP coefficients in a MB, and M denotes the number of non-zero HP coefficients in a MB
  12. 12. -12-ASSESSMENT OF CHROMA-INDUCEDPRIVACY LEAKAGE
  13. 13. -13-3.1. Objective Assessments Experimental setup  FR techniques used: PCA, FLDA, LBP  Face images: 3070 frontal face images of 68 subjects from CMU PIE (68 gallery, 340 training, and 2662 probe face images)  Probe face images represent privacy-protected face images that appear in video content originating from surveillance cameras  Performance evaluation: Cumulative Match Characteristic (CMC) curve  Notations Notation Explanation DC, LP, and HP DC, LP, and HP subband S3 DC+LP+HP S2 DC+LP S1 DC Subscripts (Y, Co, Cg) Luma and chroma channels (Y, Co, and Cg) Prime (′) Scrambled image data
  14. 14. -14-3.1. Objective Assessments Influence of distance measurement on FR effectiveness  Distance metric: Euclidean, Mahalanobis, Cosine, and Chi-square distance DE : Euclidean distance DM : Mahalanobis distance DC : Cosine distance DH : Chi-square distance  In the remainder of our experiments, we make use of the Euclidean distance metric for PCA- and FLDA-based FR, and the Chi-square distance metric for LBP-based FR
  15. 15. -15-3.1. Objective Assessments Scrambled luma information  Assumes that an adversary is not able to take advantage of the possible presence of non-scrambled chroma information in the privacy-protected probe face images
  16. 16. -16-3.1. Objective Assessments Scrambled luma and non-scrambled chroma information  We investigate whether layered scrambling is still effective when the scrambled luma channel and the non-scrambled chroma channels are simultaneously used for the purpose of automatic FR  We assume that an adversary has access to the compressed bit stream structure, and thus to the non-scrambled chroma information  We adopt feature-level fusion in order to take advantage of non- scrambled chroma information
  17. 17. -17-3.1. Objective Assessments Scrambled luma and non-scrambled chroma information
  18. 18. -18-3.1. Objective Assessments Non-scrambled chroma information
  19. 19. -19-3.2. Subjective Assessments Experimental setup  Number of observers: 32  We presented three scrambled probe face images of different subjects to the human observers for each experimental condition  Assessment method  Human observers were asked to select the gallery face image that is most similar to the given probe face image  Human observers were also able to study the probe face images at different zoom levels Gallery face images used for the subjective assessments
  20. 20. -20-3.2. Subjective Assessments Non-scrambled chroma information
  21. 21. -21-3.2. Subjective Assessments Scrambled luma and non-scrambled chroma information
  22. 22. -22-DISCUSSION & CONCLUSIONS
  23. 23. -23-4. Discussion For video surveillance applications requiring a high level of privacy protection, both the luma and the chroma channels need to be scrambled  At the cost of a higher bit rate overhead Layered scrambling for both the luma (Y) and the chroma channels (Co and Cg)
  24. 24. -24-4. Discussion Bit rate overhead Security (ideal case)  Sub-sampling decreases the level of privacy protection, given the lesser amount of data available for scrambling  Total number of combinations required to break the protection of 10 MBs is reduced from 3.6×10722 (4:4:4) to 1.7×10360 (4:2:0)
  25. 25. -25-5. Conclusions and Future Work This paper studied and quantified the influence of non-scrambled chroma infor- mation on the effectiveness of automatic and human FR Our results show that, when an adversary has access to the coded bit stream structure, the presence of non-scrambled chroma information may significantly contribute to privacy leakage For video surveillance applications requiring a high level of privacy protection, our results indicate that both luma and chroma information needs to be scrambled at the cost of an increase in bit rate overhead In order to compile a benchmark for privacy protection tools, future research will focus on identifying additional worst case scenarios
  26. 26. -26-Thank you for your attention!
  27. 27. -27-APPENDIX A Effectiveness of general-purpose visual security metrics  Visual security metrics used  Luminance Similarity Score (LSS), Edge Similarity Score (ESS), and Local Feature- based Visual Security Metric (LFVSM)  The lower the values computed by the visual security metrics, the higher the visual security
  28. 28. -28-APPENDIX B Paper of interest  Sohn, H., De Neve, W., Ro, Y.M., “Privacy Protection in Video Surveillance Systems: Analysis of Subband-Adaptive Scrambling in JPEG XR,” IEEE Transactions on Circuits and Systems for Video Technology, 21, 170–177 (2011) Book chapter of interest  Sohn, H., Lee, D., De Neve, W., Plataniotis, K.N., Ro, Y.M., “An objective and subjective evaluation of content-based privacy protection of face images in video surveillance systems using JPEG XR,” Accepted for publication in Effective Surveillance for Homeland Security: Balancing Technology and Social Issues, CRC Press / Taylor & Francis, To be published in 2013 IVY Lab video surveillance data set  http://ivylab.kaist.ac.kr/demo/vs/dataset.htm

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