000204796 001__ 204796
000204796 005__ 20190117220225.0
000204796 020__ $$a978-1-62841-484-4
000204796 0247_ $$2doi$$a10.1117/12.2083189
000204796 022__ $$a0277-786X
000204796 02470 $$2ISI$$a000354081600050
000204796 037__ $$aCONF
000204796 245__ $$aUsing False Colors to Protect Visual Privacy of Sensitive Content
000204796 269__ $$a2015
000204796 260__ $$aBellingham$$bSpie-Int Soc Optical Engineering$$c2015
000204796 300__ $$a13
000204796 336__ $$aConference Papers
000204796 490__ $$aProceedings of SPIE
000204796 520__ $$aMany privacy protection tools have been proposed for preserving privacy. Tools for protection of visual privacy available today lack either all or some of the important properties that are expected from such tools. Therefore, in this paper, we propose a simple yet effective method for privacy protection based on false color visualization, which maps color palette of an image into a different color palette, possibly after a compressive point transformation of the original pixel data, distorting the details of the original image. This method does not require any prior face detection or other sensitive regions detection and, hence, unlike typical privacy protection methods, it is less sensitive to inaccurate computer vision algorithms. It is also secure as the look-up tables can be encrypted, reversible as table look-ups can be inverted, flexible as it is independent of format or encoding, adjustable as the final result can be computed by interpolating the false color image with the original using different degrees of interpolation, less distracting as it does not create visually unpleasant artifacts, and selective as it preserves better semantic structure of the input. Four different color scales and four different compression functions, one which the proposed method relies, are evaluated via objective (three face recognition algorithms) and subjective (50 human subjects in an online-based study) assessments using faces from FERET public dataset. The evaluations demonstrate that DEF and RBS color scales lead to the strongest privacy protection, while compression functions add little to the strength of privacy protection. Statistical analysis also shows that recognition algorithms and human subjects perceive the proposed protection similarly.
000204796 6531_ $$avisual privacy protection
000204796 6531_ $$afalse color visualization
000204796 6531_ $$aobjective evaluation
000204796 6531_ $$asubjective assessment
000204796 700__ $$aCiftci, Serdar
000204796 700__ $$aKorshunov, Pavel
000204796 700__ $$aAkyuz, Ahmet Oguz
000204796 700__ $$0240223$$aEbrahimi, Touradj$$g105043
000204796 7112_ $$aHuman Vision and Electronic Imaging XX$$cSan Francisco, California, USA$$dFebruary 8-12, 2015
000204796 773__ $$j9394$$q93941L$$tHuman Vision And Electronic Imaging Xx
000204796 8564_ $$s3543806$$uhttps://infoscience.epfl.ch/record/204796/files/falsecolor-final.pdf$$yPreprint$$zPreprint
000204796 909C0 $$0252077$$pMMSPL
000204796 909CO $$ooai:infoscience.tind.io:204796$$pconf$$pSTI
000204796 917Z8 $$x212659
000204796 937__ $$aEPFL-CONF-204796
000204796 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000204796 980__ $$aCONF