Upload
yasmine-drews
View
215
Download
0
Tags:
Embed Size (px)
Citation preview
Contribution of Non-Scrambled Chroma Information in Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Images to Privacy LeakagePrivacy-Protected Face Images to Privacy Leakage
Contribution of Non-Scrambled Chroma Information in Contribution of Non-Scrambled Chroma Information in Privacy-Protected Face Images to Privacy LeakagePrivacy-Protected Face Images to Privacy Leakage
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
October 2011.
10th International Workshop on Digital-forensics and Watermarking (IWDW 2011)
ContentsContents--22--
1. Introduction1. Introduction1. Introduction1. Introduction
2. Layered Scrambling for Motion JPEG XR2. Layered Scrambling for Motion JPEG XR2. Layered Scrambling for Motion JPEG XR2. Layered Scrambling for Motion JPEG XR
3.1 Objective Assessments3.1 Objective Assessments3.1 Objective Assessments3.1 Objective Assessments
3.2 Subjective Assessments3.2 Subjective Assessments3.2 Subjective Assessments3.2 Subjective Assessments
3. Assessment of Chroma-induced Privacy Leakage3. Assessment of Chroma-induced Privacy Leakage3. Assessment of Chroma-induced Privacy Leakage3. Assessment of Chroma-induced Privacy Leakage
4. Discussion and Conclusion4. Discussion and Conclusion4. Discussion and Conclusion4. Discussion and Conclusion
1. Introduction1. 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
--44--
Privacy protected surveillance videos
1. Introduction1. 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 challenge is to concealment of privacy-sensitive regions by making use of invertible transformation of visual information at a low computational cost
--55--
1. Introduction1. 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
To limit bit rate overhead, many content-based tools for privacy protection only scramble luma information, leaving chroma information unprotected
--66--
Coding efficiency
Security level
1. Introduction1. 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 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
--77--
1. Introduction1. Introduction
FR vs. Perception-based security metrics for assessing privacy level
Luminance Similarity Score (LSS), Edge Similarity Score (ESS), and Local Feature-based Visual Security Metric (LFVSM)[1,2]
However, these metrics are general in nature and are thus not able to take advantage of domain-specific information (e.g., face information)
--88--
[1] Tong, L., Dai, F., Zhang, Y., Li, J. “Visual security evaluation for video encryption,” in: Proceedings of ACM International Conference on Multimedia, 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), (2006), 2061-2075.
2. Layered scrambling for Motion JPEG XR2. 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 scalability provisions
Layered scrambling for JPEG XR [3]
Modified JPEG-XR encoder
--1010--
LBT Q Pred.LBT
Adaptive scan
Q Pred.
Adaptive scan
Q Pred.
Scrambling(RLS) • Adaptive
entropy coding
• Fixed length coding
DC subband
LP subband
HP subband/Flexbits
Scrambling(RP)
Scrambling(RSI)
Secret key
[3] 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)
2. Layered scrambling for Motion JPEG XR2. Layered scrambling for Motion JPEG XR
Overview of layered scrambling technique
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.
--1111--
),(LRDCcoeffDCcoeff e
, ,..., , ,...,1 , 1 Cje
i xxjCiwhereLPcoeffLPcoeff
, ,
1 ,
otherwiseHPcoeff
rifHPcoeffHPcoeff e
- Random level shift (RLS) for DC subbands
- Random permutation (RP) for LP subbands
- Random sign inversion (RSI) for HP subbands
3.1. Objective assessments3.1. Objective assessments
Experimental setup FR technique 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
--1313--
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
3.1. Objective assessments3.1. Objective assessments
Influence of distance measurement on FR effectiveness Distance metric: Euclidean, Mahalanobis, Cosine, and 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
--1414--
DE : Euclidean distanceDM : Mahalanobis distanceDC : Cosine distanceDH : Chi-square distance
3.1. Objective assessments3.1. 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
--1515--
3.1. Objective assessments3.1. 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
Assuming that an adversary has access to the compressed bit stream structure, and thus to the non-scrambled chroma information
To take advantage of non-scrambled chroma information, we adopted feature-level fusion
--1616--
3.1. Objective assessments3.1. Objective assessments
Scrambled luma and non-scrambled chroma information
--1717--
3.2. Subjective assessments3.2. Subjective assessments
Experimental setup Number of observer: 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
--1919--
Gallery face images use for subjective assessment
3.2. Subjective assessments3.2. Subjective assessments
Scrambled luma and non-scrambled chroma information
--2121--
4. Discussion4. 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 to both the luma (Y) and the chroma channels (Co and Cg)
--2323--
4. Discussion4. 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
reduces from 3.6×10722 (4:4:4) to 1.7×10360 (4:2:0)
--2424--
5. Conclusions and future work5. 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
--2525--
APPENDIXAPPENDIX
Effectiveness of general-purpose visual security metrics Visual security metric used
Luminance Similarity Score (LSS), Edge Similarity Score (ESS), and Local Feature-based Visual Security Metric (LFVSM)
Lower the values computed by the visual security metrics, the higher the visual security
--2727--