2 Toward Automated Online Photo Privacy

29 Pages·2017·1.17 MB·English

Toward Automated Online Photo Privacy
ANNASQUICCIARINI,PennsylvaniaStateUniversity 2
CORNELIACARAGEA,UniversityofNorthTexas
RAHULBALAKAVI,PennsylvaniaStateUniversity
OnlinephotosharingisanincreasinglypopularactivityforInternetusers.Moreandmoreusersarenow
constantlysharingtheirimagesinvarioussocialmedia,fromsocialnetworkingsitestoonlinecommunities,
blogs,andcontentsharingsites.Inthisarticle,wepresentanextensivestudyexploringprivacyandsharing
needsofusers’uploadedimages.Wedeveloplearningmodelstoestimateadequateprivacysettingsfornewly
uploadedimages,basedoncarefullyselectedimage-specificfeatures.Ourstudyinvestigatesbothvisualand
textualfeaturesofimagesforprivacyclassification.Weconsiderbothbasicimage-specificfeatures,commonly
usedforimageprocessing,aswellasmoresophisticatedandabstractvisualfeatures.Additionally,weinclude
a visualrepresentation ofthe sentiment evokedby images. Toour knowledge,sentiment has neverbeen
used in the context of image classification for privacy purposes. We identify the smallest set of features,
thatbythemselvesorcombinedtogetherwithothers,canperformwellinproperlypredictingthedegreeof
sensitivityofusers’images.Weconsiderboththecaseofbinaryprivacysettings(i.e.,public,private),aswell
asthecaseofmorecomplexprivacyoptions,characterizedbymultiplesharingoptions.Ourresultsshow
thatwithfewcarefullyselectedfeatures,onemayachievehighaccuracy,especiallywhenhigh-qualitytags
areavailable.
(cid:2)
CCSConcepts: Securityandprivacy→Softwareandapplicationsecurity; Socialnetworkse-
curityandprivacy
AdditionalKeyWordsandPhrases:Socialnetworks,imageanalysis,privacy,machinelearning
ACMReferenceFormat:
AnnaSquicciarini,CorneliaCaragea,andRahulBalakavi.2017.Towardautomatedonlinephotoprivacy.
ACMTrans.Web11,1,Article2(March2017),29pages.
DOI:http://dx.doi.org/10.1145/2983644
1. INTRODUCTION
Online photo sharing is an increasingly popular activity for Internet users. More and
moreusersarenowconstantlysharingtheirimagesinvarioussocialmedia,fromsocial
networkingsitestoonlinecommunities,blogs,andcontentsharingsites.Sharingtakes
placebothamongpreviouslyestablishedgroupsofknownpeopleorsocialcircles(e.g.,
Google+, Flickr, or Picasa) and also increasingly with people outside the user’s social
circlesforpurposesofsocialdiscovery[Blog2012]tohelpthemidentifynewpeersand
learnaboutpeers’interestsandsocialsurroundings.Forexample,peopleonFlickror
Pinterest can upload their images to find social groups that share the same interests
ThisworkissupportedbytheNationalScienceFoundation,undergrant1421776andgrant1421970.
Authors’ addresses: A. Squicciarini, 301D IST Building, College of Information Sciences and Technology,
Pennsylvania State University, University Park PA 16802; email: [email protected]; C. Caragea,
F228DiscoveryPark,ComputerScienceandEngineering,UniversityofNorthTexas,Denton,TX76203,
USA;email:[email protected];R.Balakavi,317ISTBuilding,CollegeofInformationSciencesandTechnol-
ogy,PennsylvaniaStateUniversity,UniversityParkPA16802;email:[email protected].
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DOI:http://dx.doi.org/10.1145/2983644
ACMTransactionsontheWeb,Vol.11,No.1,Article2,Publicationdate:March2017.
2:2 A.Squicciarinietal.
[Blog2012;Zhengetal.2010].However,semanticallyrichimagesmayrevealcontent-
sensitive information [Ahern et al. 2007; Squicciarini et al. 2011; Zerr et al. 2012].
Consideraphotoofastudent’s2014NewYears’publicceremony,forexample.Itcould
beshared withinaGoogle+ circle orFlickr group, or itcould beusedtodiscover 2014
awardees.Here,theimagecontentmaynotonlyrevealtheusers’locationandpersonal
habitsbutmayunnecessarilyexposetheimageowner’sfriendsandacquaintances.
Sharing images within online content sharing sites, therefore, may quickly lead to
unwanteddisclosureandprivacyviolations[Bullguard2014;Ahernetal.2007;Besmer
and Lipford 2009]. Malicious attackers can take advantage of these unnecessary
leaks to launch context-aware attacks or even impersonation attacks [Higgins 2010],
asdemonstratedbyaproliferatingnumberofcasesofprivacyabuseandunwarranted
access.
In particular, privacy of online images is inherently a subjective matter, dependent
ontheimageowner’sprivacyattitude,awareness,andtheoverallcontextwhereinthe
imageistobeposted.
Inthiswork,weexplorethehypothesisthatsomegenericpatternsofprivateimages
can be well identified when a group of online images are taken into consideration,
regardlessoftheirauthors’individualprivacybiasandlevelofawareness.
Toward validating this hypothesis, we carry out an extensive study aiming at ex-
ploring the main privacy and sharing needs of users’ uploaded images. Our goal is
to develop learning models to estimate adequate privacy settings for newly uploaded
images,basedoncarefullyselectedimage-specificfeatures.Wefocusonimage-specific
features only, rather than broader contextual social network dimensions or personal
informationabouttheimageposterorhis/heraudience.Intuitively,contextualfeatures
may help in addressing our research question but would require much more informa-
tion for every image, which may or may not always be available or even reliable. We
aim to minimize additional personal information that would be needed to infer users’
privacy preferences. To achieve this goal, we focus on two types of image features:
visual-contentfeaturesandimages’metadata.
Within these feature types, we aim to identify the smallest set of features that, by
themselvesorcombinedtogetherwithothers,canperformwellinproperlypredicting
the degree of sensitivity of users’ images. Among the features that we use to capture
thevisual-contentofimages,weincludebothlow-levelimageprocessingfeaturesthat
capturecolors,patterns,andedgedirections,withmoresophisticatedderivedfeatures.
One such derived feature is “sentiment,” representing the sentiment evoked by the
image.Ourhypothesishereisthatthesentimentevokedbyanimagemaybecorrelated
withthetypeofdisclosureassociatedwithit.Tothebestofourknowledge,sentiment
hasneverbeenstudiedincorrelationwithprivacyclassificationofimages.
Wedevelopandcontrastvariouslearningmodelsthatcombineanincreasinglylarge
number of features using both combined and ensemble classification methods. Our
analysis shows some interesting performance variability among all the analyzed fea-
tures,demonstratingthatwhilemodelsforimages’privacycanbewellcapturedusing
alargeamountoffeatures,onlysomeofthemhaveasignificantdiscriminativepower.
Tothisdate,onlyveryfewstudieshavestartedtoaddressthiscomplexproblem.Most
recent work related to online disclosure of personal information has been devoted to
protectinggenerictextualusers’onlinepersonaldata,withnoemphasisontheunique
privacychallengesassociatedwithimagesharing[LiuandTerzi2010;Heetal.2006].
Further, work on image analysis has not considered issues of privacy but focused on
semanticmeaningofimagesorsimilarityanalysisforretrievalandclassification(e.g.,
Chapelle et al. [1999], Sawant et al. [2011], Ng et al. [2007], Datta et al. [2008], and
daSilvaTorresandFalca˜o[2006]).Onlysomerecentworkhasstartedtoexploresimple
classificationmodelsofimageprivacy[Squicciarinietal.2011;Zerretal.2012].
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TowardAutomatedOnlinePhotoPrivacy 2:3
WespecificallyidentifiedScale-InvariantFeatureTransformation(SIFT)andTAGS
(imagemetadata)asthebest-performingfeaturesinavarietyofclassificationmodels.
We achieve a prediction accuracy of 90% and a Break Even Point (BEP) of 0.89 using
thesefeaturesincombination.IntheabsenceofTAGS,ourresultsshowthatSIFTand
Sentiment-basedfeatureperformthebest,withpredictionaccuracyreachingunto80%.
Furthermore, we analyze privacy needs of images on a multi-level scale, consistent
withcurrentprivacyoptionsofferedbymostpopularWeb2.0sharingsitesandappli-
cations. We adopt a five-level privacy model, where image disclosure can range from
open access to disclosure to the owner only. In addition to the five-privacy levels, our
modelsalsoincludevariousdegreesofdisclosureforeachimagetomodelthedifferent
ways an image can be made available online. These degrees of disclosure are View,
Comment,Download.Accordingtothismulti-level,multi-classprivacyframework,we
buildmodelstoestimateadequateprivacysettings,usingthebestcombinationoffea-
turesobtainedinourprivacypredictionmodelsforbinaryclassification.Inthesenew
models,weaccountfortheinter-relationsbetweendifferentprivacyclasses.Anexam-
ple for such an inter-relation is the following: An image can be downloadable only if
it can be viewed. To model these inherent inter-relations, we used Chained classifier
[Read et al. 2011] models, where predicted class labels are used to predict new class
labels. Our experiments confirm that these models, executed using a blend of visual
andmetadatafeatures,consistentlyoutperformedstrongbaselinemodels.
Tothebestofourknowledge,thisisthefirstandmostcomprehensivestudycarried
out to date on large-scale image privacy classification that includes not only simple
privacy classificationbased onbinary labelsbut alsomodels formore complex, multi-
facetprivacysettings.
Therestofthearticleisorganizedasfollows.WediscusspriorresearchinSection2.
In Section 3, we elaborate our problem statement, whereas in Section 4 we discuss
differentimage-basedfeaturesthatweexplored.InSection5,weanalyzethepatterns
ofvisualandtextualfeaturesinpublicandprivateimages.InSection6,weintroduce
the multi-class model. We finish our analysis in Section 7, where we discuss pointers
tofutureworksandconcludethearticle.
2. RELATEDWORK
A number of recent studies have analyzed sharing patterns and social discovery in
imagesharingsiteslikeFlickr[Choudhuryetal.2009;AmesandNaaman2007;Miller
andEdwards2007;Zhengetal.2010].Amongotherinterestingfindings,scholarshave
determined that images are often used for self- and social disclosure. In particular,
tags associated with images are used to convey contextual or social information to
thoseviewingthephoto[Sawant2011;PlangprasopchokandLerman2007;Chenetal.
2008;AmesandNaaman2007;Henneetal.2013],motivatingourhypothesisofusing
metadataasoneamongotherfeaturesforprivacyextraction.
MillerandEdwards[2007]furtherconfirmthatpeoplewhosharetheirphotosmain-
tainsocialbondsthroughtaggingtogetherwithonlinemessaging,commenting,andso
on.Theyalsoidentifytwodifferenttypesofusers(normalandpowerusers),indicating
the importance of interpersonal differences, and that users may have different levels
of privacy concerns depending on their individual level of privacy awareness and the
imagecontent.
Ahern et al. [2007] analyzed effectiveness of tags as well as location information in
predicting privacy settings of the photos. Further, they conducted an early study to
establish whether content (as expressed by image descriptors) is relevant to image’s
privacysettings.Basedontheiruserstudies,contentisoneofthediscriminatoryfactors
affectingimageprivacy,especiallyforimagesdepictingpeople.Thissupportsthecore
idea underlying our work: that particular categories of image content are pivotal in
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establishingusers’imagessharingdecisions.JonesandO’Neill[2011]laterreinforced
theroleofprivacy-relevantimageconcepts.Forinstance,theydeterminedthatpeople
are more reluctant to share photos capturing social relationships than photos taken
for functional purposes; certain settings such as work, bars, and concerts cause users
to share less. These studies also revealed significant individual differences within
the same type of image, based on some explanatory variables relating to the identity
of the contacts and the context of photo capture, providing insights into the need
for customized, subjective models for privacy patterns. Zerr and colleagues recently
developed PicAlert [Zerr et al. 2012], which carries out content analysis for image
privatesearchanddetectionofprivateimages.
Along the same theme, Besmer and Lipford [2009] pointed out that users want to
regain control over their shared content but, meanwhile, they feel that configuring
proper privacy settings for each image is a burden. Similarly, related work suggests
sharing decisions may be governed by the difficulty of setting and updating policies,
reinforcing the idea that users must be able to easily set up access control policies
[AlessandraMazzia2011;Vyasetal.2009;CheekandShehab2012;Squicciarinietal.
2011; He et al. 2006; Liu and Terzi 2010]. Some notable recent efforts to tackle these
problems have been conducted in the context of tag-based access control policies for
images[Yeungetal.2009;Klempereretal.2012;Vyasetal.2009],showingsomeinitial
successintyingtagswithaccess controlrules.However, thescarcityoftagsformany
online images [Sundaram et al. 2012], and the workload associated with user-defined
tags precludes accurate analysis of the images’ sensitivity based on this dimension
only.Otherwork[FangandLeFevre2010;CheekandShehab2012;AlessandraMazzia
2011;Bonneauetal.2009a,2009b;Heetal.2006]hasfocusedongenericusers’profile
elements and typically leveraged social context rather then users’ individual content-
specificpatterns.
A loosely related body of work is on recommendation of tags for social discovery
[Sawant 2011; Plangprasopchok and Lerman 2007; Yu et al. 2010; Chen et al. 2008]
andforimageclassification[SanPedroandSiersdorfer2009;Yuetal.2009;Chenetal.
2008]inphotosharingwebsiteslikeFlickr.Intheseworks,thetypicalapproachisfor
authorstofirstcollectadequateimagesandthenclassifyimagesaccordingtovisualand
context-relatedfeatures.Afterusersuploadtheirimages,theserverextractsfeatures
andthenclassifiesandrecommendsrelevanttagsandgroups.
AlsorelatedistheworkfromHenneandcolleagues[Henneetal.2013],whoprovided
an extended analysis of privacy threats as they arise from photos in popular online
content sharing sites, such as Flickr. As noted by Henne and others [Madejski et al.
2012;Xuetal.2015],privacythreatsresultfromeitherauser’sownactionsorshared
photos or are unintentional, as a consequence of others’ photo uploads. We note that
bothtypesofthreatsareimportantandofincreasingrelevanceandcanyieldto“errors”
or unintended issues. In this work, we keep our focus on image protection based on
accesspolicies,undertheimplicitassumptionsthattheseareappliedbyoneauthorized
entity, that is, the image uploader. We note, however, that our work is agnostic to the
problemofownership:Apolicycouldbeappliedeitherbythepartywhoownstheimage
or by a third party seeing him- or herself tagged or exposed in an image uploaded by
others. We acknowledge that this may raise some interesting new research questions
thatweplantoexploreinthefuture.
Finally, there is a large body of work on image content analysis for classification
and interpretation (e.g., Chapelle et al. [1999], Sawant et al. [2011], Ng et al. [2007],
Vailayaetal.[1998],ZhuangandHoi[2010],Wangetal.[2009],andDengetal.[2010]),
retrieval (Datta et al. [2008], da Silva Torres and Falca˜o [2006], He et al. [2002], and
Chatzichristofis et al. [2009] are just some examples), and photo ranking [Sun et al.
2009;Yehetal.2010],alsointhecontextofonlinephotosharingsites,suchasFlickr
[San Pedro and Siersdorfer 2009; Yu et al. 2009; Chen et al. 2008; Sundaram et al.
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2012;Rabbathetal.2011,2012;Choudhuryetal.2009].Thispreviousworkisuseful
inidentifyingmeaningfulcontent-basedfeaturesforeffectiveimagecontentextraction,
discussedinSection4.
We presented a preliminary version of this work in Squicciarini et al. [2014]. In
this extended work, we extend previous contribution in a number of ways. First, we
introduceanewperspectiveintheanalysisoftheimages,inthatweintroduceanew
complex semantic feature that has never been linked to image privacy, that is, senti-
mentfeature.Weshowhowthesentimentanimageevokeshelpsinlinkinganimage’s
disclosure setting. Further, we also perform an in depth analysis of outliers, carry a
largeamountofnewexperiments,andprovideanewin-depthsetofconsiderationson
theroleoftheanalyzedfeaturesinhelpinguncoverprivacypatterns.
3. PROBLEMSTATEMENT
Theobjectiveofourworkistoinferadequateprivacysettingsforonlineimages,based
onacommunityperspectiveofgeneralusers’privacypreferences.
Our goal is twofold: (1) We aim to identify a variety of visual features that can be
informative in profiling images’ privacy needs, and (2) among the identified features,
we wish to determine the smallest set of features that, by themselves or combined
togetherwithothers,canperformwellinproperlydefiningthedegreeofsensitivityof
users’ images. We note that these are challenging objectives, as the classification we
hopetoachieveisbasedonthesubjectivenotionofprivacythatthereforeattemptsto
assigna“semantic”meaningtoanimageratherthansimplydescribeitsmaincontent
(orextractthecontext).
Our approach is to consider both images’ visual content and their associated meta-
data. The intuition underlying content-based features is that, as demonstrated in re-
centwork(e.g.,BesmerandLipford[2009]),althoughprivacyisasubjectivedecision,
certain visual content is likely personal or too sensitive to be disclosed to a public
online audience. Hence, we expect that certain visual elements of an image, like the
presence of edges, its color, its predominant elements, or the presence of faces, may
give some insights about its degree of privacy. Our content-specific features include a
selection of both “basic” features commonly adopted in image processing, as well as
more abstract, sophisticated features. Among others, we experiment with sentiment
features.Sentiment-relatedfeaturesrefertotheabilityofcapturingtheemotionsand
feelingsreflectedbyanimage.
On the contrary, metadata, typically defined in terms of keywords extracted from
tags or captions, can provide insights into the image’s context, that is, where it was
taken,whatitrepresentstothelabeler(e.g.theimageowner),whatfeelingsitevokes,
andsoon.
Additionalcontextualdimensionsarepurposelynotconsideredforthepurposeofthis
study. For instance, we do not consider any additional social networking or personal
informationaboutthephotoownersandthesitewheretheimagewasoriginallyposted,
as we aim to leverage to the extent possible the content carried by the image itself.
Further,informationaboutaphotoposterandhisorheronlinesocialnetworkactivities
maynotbeavailableoreasilyaccessible.
Our learning models try to address the stated goals using a blend of visual and
metadata features using two alternative privacy models. First is a binary model, and
this accounts for the case of an image that is either to be disclosed or not (public vs.
private).Thesecondprivacymodelaccountsforthemorecomplexcaseofanimagetobe
placedinanonlinesocialnetworkingsite,whereusersmaychoosefromafine-grained
setofoptions(i.e.,shouldafriendviewtheimages?shouldtheybeallowedtodownload
it?shouldfamilymembersbeallowedtoviewandorcommentontheimage?).Inthis
case, privacy settings will be defined by multi-option privacy privileges and various
disclosureoptions.
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4. IMAGERELEVANTFEATURES
Inthissection,wediscusstheimagefeaturesconsideredforouranalysis.
4.1. VisualFeatures
We are interested in identifying a few pivotal features that can be useful for image
classificationwithrespecttoprivacy.Wenextdescribeourselectedvisualfeaturesand
providesomeobservationsfromtheuseofthefeaturesforprivacymodels.
—SIFT [Lowe2004b].Asanimage’sprivacylevelmaybedeterminedbythepresence
ofoneormorespecificobjectsratherthanallofthevisualcontent(e.g.,thinkabout
an image with somebody carrying a gun and the same image with the person hold-
ingflowersinstead),featuresabletodetectinterestingpointsofimagesareneeded.
SIFT, being one of the most widely used features for image analysis in computer
vision, is such a feature. It detects stable key point locations in the scale space of
animage.Insimpleterms,theSIFTapproach istotakeanimageandtransformit
into a “large collection of local feature vectors” [Lowe 2004a]. Each of these feature
vectors is invariant to any scaling, rotation, or translation of the image. We ex-
tractanimageprofilebasedonthestate-of-the-artmodelcalledbag-of-visual-words
(BoW) [Sivic and Zisserman 2003; Yang et al. 2007], which can effectively evaluate
theimagesimilarityandiswidelyusedinimagesimilaritysearch,imageretrieval,
and even content-based social discovery [Sawant 2011]. The image BoW vector is
first obtained by extracting the features of preferred images and then clustering
them into the visual word vocabulary (cid:3), where each element is the distinct word
occurrence. Features are extracted for each image and each element of the feature
vectorismappedontooneofthebagofwordsand,oncealltheelementsarechecked,
wegetasequenceofnumberswhoselengthisequaltothelengthoftheBoW.Each
numberrepresentsthenumberofelementsintheoriginalSIFTfeaturevector,which
havebeenmappedontothecorrespondingvisualword.Asaresult,animageprofile
is created S = {s ,...,s }, where s reflects the strength of image’s preference on
1 m i
wordw andmisthesizeof(cid:3).
i
—Sentiment. The emotions evoked by an image may be tightly related to an image’s
privacy needs. Accordingly, we leverage a Visual Sentiment Ontology (VSO) to de-
tectsomesentiments.AVSOisconstructedbasedonunderstandingthatthevisual
conceptsarestronglyrelatedtosentiment[Borthetal.2013].Moreprecisely,VSOis
built on psychological theories and web mining comprising sentiment carrying con-
ceptscalledAdjectiveNounPairs(ANPs).CuteDog,BeautifulDay,DisgustingFood
are a few examples of ANPs. A set of such strong sentiment-carrying concepts are
collectedandeachoftheseconceptsareencodedwithvisualinformationpertaining
to images that are relevant to that ANP. The detector library identifies a total of
1,200concepts.Givenatestimage,theANPdetectorframeworkoutputsaseriesof
1,200 decimal numbers, each in the range of 0–1. Each of these numbers indicates
thedegreeofpresenceofthecorrespondingANPinthetestimage.
—RedGreenBlue(RGB).Imageswithagivencolorandtexturepatternscanbemapped
intocertainclasses,basedonwhatislearnedfromthetrainingset.Forexample,in-
stances with a pattern of green and blue may be mapped to public images, being
indicative of nature. Accordingly, we include the RGB feature to extract these po-
tentially useful patterns. RGB is a color space for image representation, and Hue,
Saturation, and Brightness (HSB) values can be obtained, for example, by a color
space conversion from RGB to HSB, obtaining a feature detector. The feature de-
tectorcomponentsarethereforeRed,Green,Blue,Hue,SaturationandBrightness.
Valuescorrespondingtoeachofthevariablesareextractedfromanimage,andeach
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Fig.1. FACIALdetectionandextraction-examples.
feature is encoded into a 256byte length array. This array is serialized in sparse
formatwhereeachinstancecorrespondstothefeaturevectorofanimage.
—FACIAL Detection. Images revealing one’s identity are more likely to be considered
private [Ahern et al. 2007], although this is subjective to the specific event and
situation wherein the image was taken. Henceforth, to discriminate to the extent
possiblebetweenpurelypubliceventswithpeopleandotherimagesinvolvingvarious
individuals, we detect the ratio that the area of faces take in the image to identify
whethertheyareornotprevalentelementsintheimage.
WeextractFACIALkeypointsusingtheFKEFaceDetectorframework.Information
aboutpresenceoffacesisencodedintwoattributesforeachimage,similarlytoZerr
et al. [2012]. One attribute represents the number of faces found in the image and
thesecondattributeindicatestheamountofareaoccupiedbyfacesintheimage.
The framework detects faces that are straight up and clearly visible. In some
images,thoughtherearefacesvisible,duetovariousfactors,thefacescouldnotbe
detected. Images with faces not clearly visible, images with dark backgrounds, and
images that show faces from acute angles are a few factors that can result in faces
notbeingdetectedbytheAPI(ApplicationProgrammingInterface).InFigure1,we
showtwosetsofimageswhereFACIALdetectionissuccessfulandwhereitfails.
—EDGEDirectionCoherence.Asmoreandmoreusersenjoythepervasivenessofcam-
eras and smart devices, the number of online images that include some “artistic”
content (landscapes, sceneries, etc.) is also increasing. Hence, we would like to in-
clude a feature that can help with capturing similarities in landscape images. One
such feature that has proven to be useful for models on landscape images is EDGE
Direction Coherence, which uses EDGE Direction Histograms to define the texture
inanimage.Thefeaturestoresthenumberofedgepixelsthatarecoherentandnon-
coherent.Adivisionofcoherenceandnon-coherenceisestablishedbyathresholdon
thesizeoftheconnectedcomponentedges.Thisfeatureuses72binstorepresentco-
herentpixelsandonebinfornon-coherentpixels.Afterseparatingoutnon-coherent
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Fig.2. ExampleofaFlickrimageanditstags.
pixels,webacktrackfromarandomcoherentpixeltoanotherandcheckifitiswithin
5◦ andthenupdatethecorrespondingcoherentbins[Vailayaetal.1998].
4.2. MetaData
Annotationofonlineimagesisnowcommonpractice,anditisusedtodescribeimages,
as well as to allow image classification and search by others. Users can tag an image
byaddingdescriptivekeywordsoftheimagescontentforpurposesincludingorganiza-
tion, search, description, and communication. For instance, each image in Flickr has
associated one or more user-added tags, as well as a set of Flickr-generated tags. In
thiswork,wefocusedonuser-generatedtagsonlyandcreatedafeaturevectoroftags
for each image accordingly. We created a dictionary of words from all images in the
training set such that there are no duplicates (in the dictionary) and applied basic
stemmingmethodstolimitthenoiseintroducedbymisspelling,useofplurals,andso
on. Once we have the dictionary ready, each feature vector is represented in sparse
format,where anentry ofthevector corresponds toaword. Eachunique word thatis
a tag for an image has an entry in the vector. This sparse representation allows for a
compactfeaturevectorforeachinstance,removingunnecessaryinformationaboutthe
absenceofkeywords,whichareinthedictionaryandnotintheimage.
Accordingly,wetrytocorrelateimagesbyusingthefeaturevectortocaptureusage
of the same tags. We observe that most of the images that show similar descriptive
patternshaveextensivewordusagethatissimilar.AnexampleoftagsusageinFlickr
isgiveninFigure2.Forthisimage,theassociatedwordsarebeach,water,andocean,
which all have a high degree of similarity. Similar findings are reported for other
images, where tags appear to be extremely useful: As we further elaborate in Section
5.2.4, tags are a predominant feature for privacy classification purposes, although
acceptableresultsarefoundevenintheabsenceofavailablemetadata.
5. PRIVATEVERSUSPUBLICIMAGES:EMPIRICALANALYSIS
Ouranalysisincludestwokeysteps.First,weanalyzealargelabeleddatasetofimages
posted online, by means of unsupervised methods, to identify the main distinctions
betweenprivateandpublicimages.Second,weinvestigateprivacyimagesclassification
models, taking into account the results of our clustering analysis and the features
discussedintheprevioussection.
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Weemploytwodatasets.Forthefirstdataset,wetookasampleofFlickrimagesfrom
thePicAlertdataset[Zerretal.2012].ThePicAlertdatasetincludesrandomlychosen
Flickr images on various subjects and different privacy sensitivity.Each image in the
datasetislabeledusingprivateandpubliclabelsbyrandomlyselectedusers.Wefocus
on about 6,000 images randomly sampled from the original dataset to include actual
online images (i.e., still on the site) with associated keywords. The dataset includes
publicandprivateimagesintheratioof2:3.Theseconddatasetwassampledfromthe
Visual Sentiment Ontology repository [Borth et al. 2013]. The repository has a good
blendoflandscapeimages,animals,artwork,peopleandsoon.About4,000FlickrURL
(UniformResourceLocator)wererandomlysampledfromthedataset.Theassociated
keywordswereextractedfromtheFlickrsitedirectly.
Some of the privacy labels were already part of the first dataset, whereas we
use crowdsourcing methods (i.e., Amazon Mechanical Turk (AMT)) for labeling the
additional datasets and complement existing labels with more complex settings (see
Section6).
Ofcourse,similarlytoZerretal.[2012],theadoptedprivacylabelsonlycapturean
aggregatedcommunityperceptionofprivacyratherthanahighlysubjective,personal
representation. We argue that the provided representation is is correlated to textual
and visual features in a plausible way and can be predicted using carefully crafted
classification models. We also believe that using a dataset reflecting a community,
rather than a dataset created from a single author, poses some interesting unique
challenges. Because images are not linked to individual users and the image authors
do not label them with their privacy preferences, it is non-trivial to find classification
modelsandthesetoffeaturesthatcanhelpextractthem.
5.1. CharacteristicsofPrivateandPublicImages
To understand what makes images private or public, we first explored some of the
consistentcharacteristicsamongeachofthesetwoclasses,consideringbothvisualand
metadataelements.
5.1.1.VisualDifferencesbetweenPublicandPrivateImages.Wefirstexploredwhetherthere
areanyconsistenttypesofimagesorimagecontentthatcanhelpdefineprivateversus
publicimages.
Ourapproachtoidentifythesecharacteristicsistogroupimagesbycontentsimilarity
to explore the visual similarities that define the clusters. To this end, we used unsu-
pervised learning methods. In particular, we applied the Java API for Content Based
Image Retrieval (CBIR) from Latha and colleagues [Latha 2011]. This implementa-
tion performs image clustering to identify clusters among public and private images,
respectively.TheAPIuseswavelet-basedcolorhistogramanalysisandenhancements
providedbyHaarwavelettransformation.Withcolorhistograms,theimageundercon-
sideration is divided into sub-areas and color distribution histograms are calculated
for each of the divided areas. The wavelet transformation is used to capture texture
patternsandlocalcharacteristicsofanimage.Animageretrievalalgorithmisusedfor
imageretrievalbasedonsimilarity.
On running CBIR, we observed similarity patterns among images in different sets
that were clustered. Most public images belong to one of three categories as follows:
(1)WomenandChildren,(2)Symbolsandblack-and-whiteimages,(3)Artwork.Private
images could be mainly grouped into (1) People and (2) Sketches. Note that while
thereissomeoverlapamongthesecategories,theimageswithwomenandchildrenin
the public categories portray mostly photos taken in public settings, whereas private
imagesportrayingpeopleareclose-upimageswithmoreskinexposed.
ACMTransactionsontheWeb,Vol.11,No.1,Article2,Publicationdate:March2017.
2:10 A.Squicciarinietal.
Asafirstobservation,wenotethatnotallimagesofpeopleareprivate.Ourclusters
show that images indicative of people’s life events, personal stories, and so on, are
consideredequallyconfidential.Second,imageswithchildrenorhumansingeneralare
equallyclassifiableasprivateorpublic,dependingonthespecificvisualrepresentation
intheimage.Finally,sketchesappeartobedeemedsensitive.Wespeculatethesemay
belabeledasprivate,astheymayrepresentpersonalaspectsofone’slife,likeatattoo
or a piece of art.1 These observations confirm that simply considering the presence
of people as the only relevant feature may not be sufficient (we provide additional
resultsonthisaspectinthenextsection)andthatmultiplefeaturesareneeded,both
visually (to describe the content in the cluster classes) and through text, to provide
somecontextualinformation.
5.1.2.KeywordsPatternsinPublicandPrivateImages.To further our understanding of
the images and their privacy needs, we analyzed the keywords associated with each
image.Specifically,weenrichedthedatasetbyaddingannotationsforeachimageinthe
datasetandextractedtheseannotationsfromtheFlickr’staggingsystems.Eachimage
in Flickr has associated one or more user-added tag, which we crawled directly from
Flickr,asitwasnotpartoftheoriginaldataset.Toobtainkeywordgroupsreflectiveof
the most generic topics used to tag and index the images, we clustered images based
onkeywordsimilarity.
Precisely,weperformedkeywordhypernymanalysisoverallofthekeywordsassoci-
atedwiththeimages[Squicciarinietal.2011],usingWordnetasareferencedictionary.
For each keyword t, we created a metadata vector listing the hypernyms associated
i
with the word. After extracting all the hypernyms of all the keywords for an image,
we identified a hypernym per part of speech. We identified all the nouns, verbs, and
adjectives in the metadata and stored them as metadata vectors τ = {t ,t ,...,t},
noun 1 2 i
τverb = {t1,t2,...,tj}, and τadj = {t1,t2,...,tk}, where i, j, and k are the total number
of nouns, verbs, and adjectives, respectively. This selection was done by choosing the
hypernym that appeared most frequently. In case of ties, we choose the word that is
closesttotherootorbaseline.
We repeated the same procedure over different parts of speech, that is, noun, verb,
and adjective. For example, consider a metadata vector τ = {“cousin,” “first steps,”
“babyboy”}.Wefindthat“cousin”and“babyboy”havethesamehypernym“kid,”and
“first steps” has the hypernym “initiative.” Correspondingly, we obtain the hypernym
list η = {(kid, 2), (initiative, 1)}. In this list, we select the hypernym with the highest
frequency to be the representative hypernym, for example, “kid.” In the case where
therearemorethanonehypernymwiththesamefrequency,weconsiderthehypernym
closest to the most relevant baseline class to be the representative hypernym. For
example, if we have a hypernym list η = {(kid, 2), (cousin, 2), (initiative, 1)}, we
will select “kid” to be the representative hypernym, since it is closest to the baseline
class “kids.” Once we computed the representative hypernyms for each instance, the
next step was to cluster the instances based on the hypernyms. This was achieved by
calculating the edit distance of each existing cluster center with a new instance and
the weighted average distance is compared to a threshold value. The new instance is
addedtoacluster,oncetheeditdistancebetweenthecorrespondingclustercenterand
theinstanceisbelowthethreshold.Ifthedistancefromnoneoftheclustercentersfalls
belowthethreshold,thenthenewinstanceisaddedasapartofanewclusterandthe
instance is made the cluster center. In addition, existing clusters keep updating their
cluster centers as new instances are added. A cluster center represents a noun, verb,
1Notethatourobservationsaremainlyqualitative;tofullygraspthedifferenceoftheseclasses,onemay
needhumananalysisandadditionalimageprocessingwork,whichgoesbeyondthescopeofthiswork.
ACMTransactionsontheWeb,Vol.11,No.1,Article2,Publicationdate:March2017.