000204796 001__ 204796 000204796 005__ 20190416055544.0 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 $$ooai:infoscience.tind.io:204796$$pSTI$$pconf$$qGLOBAL_SET 000204796 917Z8 $$x212659 000204796 937__ $$aEPFL-CONF-204796 000204796 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED 000204796 980__ $$aCONF