# Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services

Muhammad Umair, Athman Bouguettaya, and Abdallah Lakhdari M. Umair, A.
Bouguettaya, A. Lakhdari are with the School of Computer Science, University
of Sydney, Australia. E-mail: {muhammad.umair, athman.bouguettaya,
<EMAIL_ADDRESS>
###### Abstract

We propose a novel framework for crowdsourced images to determine the
likelihood of an image being fake. We use a service-oriented approach to model
and represent crowdsourced images uploaded on social media, as image services.
Trust may, in some circumstances, be determined by using only the non-
functional attributes of an image service, i.e., image metadata. We define
intention of changes as a key parameter to ascertain fake image services. A
novel framework is proposed to estimate the intention of underlying changes
considering change in semantics of an image. Our experiments show high
accuracy using a large real dataset.

###### Index Terms:

Image’s metadata, scene reconstruction, image as a service, modified images,
trust, intention, image’s non-functional part, fake images.

##  1 Introduction

Social media has become a key platform to share news and information related
to real-life events. There are approximately 5 billion active users on social
media [1]. Social media users publish a large amount of data related to public
events [2]. These crowdsourced social media streams may possess some critical
information that describes a situation from different perspectives [3]. For
instance, a social media stream may unveil some unfolding situations in a road
accident [4]. Utilizing these crowdsourced images can significantly facilitate
the task of scene analysis and reconstruction.

We use the term image service to refer to a social media image. In this
respect, we define an image service to have functional and non-functional
properties. Functional attributes are related to the capturing of an image,
e.g., switching picture/video modes, panorama mode, delayed/timed picture
taking, etc. Whereas, non-functional attributes help in distinguishing
different image services, e.g., subject distance, camera elevation angle,
resolution, color space, etc.

Existing work on image services focuses on image service selection and
composition techniques for scene analysis and reconstruction [5]. A
fundamental assumption in the existing scene reconstruction approaches has so
far been that image services are intrinsically trustworthy. However, the trust
issue is paramount when image services are assumed to be crowdsourced [6].
Defining trust in crowdsourced environments has traditionally been addressed
using natural language processing and information retrieval techniques [7].
These approaches are usually costly in terms of computation and resources. A
preliminary work on a service-based trust framework for image services is
described in [8, 9]. This particular work is based on the users’ comments in
an image service to assess its credibility [9]. The credibility of image
services may not be completely assessed based only on the user’s stance [10].

We propose to assess trust using an objective framework consisting of
modifications and updates in an image service. Changes in an image service are
often reflected in its non-functional attributes [11]. For instance, a falsely
claimed location in a picture can be confirmed using the GPS and other spatial
tags in the image metadata [12]. We leverage only the non-functional
attributes to determine the likelihood of an image to be fake. We define fake
as a context-sensitive parameter that reflects whether the changes in an image
are harmful for a particular application. For instance, an image which is
considered as fake for reconstructing a road accident scene may not be fake
for analyzing a public march. Therefore, changes in image services should be
extensively investigated before concluding on the fakeness of an image. In
this respect, we propose a detailed framework to analyze modifications in
image services. We approach this problem from social media owners perspective
implying that we have access to the metadata and ground truth of the image.

We define intent of changes as a key parameter to ascertain fake image
services. Intention reflects the purpose of introducing changes in images.
Changes can be either ill-intentioned or well-intentioned. For instance,
hiding the facts in an image service are usually ill-intentioned changes
whereas improving the outlook of an image is mostly considered as a well-
intentioned change. We propose to estimate the intention of modifications by
determining the change in semantics of an image. In this respect, we introduce
a novel way of employing Latent Semantic Analysis (LSA) to extract semantics
of an image service. LSA forms image vectors with the values representing the
importance of different latent contexts in an image. Cosine similarity measure
is then applied on these image vectors to quantify semantic differences among
images. We formalize intention in terms of these semantic changes in an image.
Afterwards, the intention is used to determine the likelihood of an image
being fake in a specific context. The proposed method is valid for a typical
set of modifications that are reflected in non-functional part of an image
service. Some changes may not be reflected in non-functional part of an image
service, e.g., changes in shades and intensity of the colors. We consider only
those changes which are reflected in the non-functional attributes
specifically the spatio-temporal and contextual attributes. Experiments are
conducted on two different large real datasets for evaluation. We use accuracy
and time efficiency metrics to evaluate the proposed framework. It achieves
80-95% accuracy on a systematic set of experiments. The following are our main
contributions:

  * •

A novel services-based framework is proposed to determine fake image services.
The framework only relies on the non-functional attributes of an image service
and does not require processing the image.

  * •

Intent of changes is defined as an imperative parameter to ascertain fake
image services. A novel way is introduced to formalize intention based on the
change in semantics of an image service.

  * •

LSA is employed in a unique way to determine semantics of an image and to
quantify the semantic changes.

##  2 Motivating Scenario

Figure 1: Motivating Scenario

We use a scene of a road accident as our motivating scenario. Figure 1 shows
images of a road accident uploaded on social media. Before the accident, a
bike rider was standing at a side of a road while a bulldozer was working on
the other side. The bulldozer lost control and started moving towards the bike
rider. The bike rider was about to be hit by the bulldozer while a speeding
Sport Utility Vehicle (SUV) crashed into the bulldozer right before him (see
original images in Figure 1 marked with the green ticks). We take this scene
as an example to demonstrate how the modifications may lead to erroneous
conclusions. In this respect, we remove the bulldozer from some images
(modified images in Figure 1). Modified images give an impression that the
accident took place due to the negligence of the SUV’s driver.

We apply the existing service-oriented approach proposed in [13] to compose
relevant image services. Non-functional attributes of image services are only
utilized in these methods to reconstruct the scene. These scene reconstruction
techniques are based on the spatio-temporal relevancy of the image services.
They do not consider modifications in image services and hence, are unable to
filter out the modified images. Scene A in Figure 1 is reconstructed by the
existing image service selection approaches. It can be observed that the scene
consists of some modified images giving a wrong impression that the accident
took place due to the negligence of the SUV’s driver.

There is a need to intrinsically evaluate the image services before using them
for scene reconstruction. The proposed framework can be utilized to filter out
the untrustworthy images while reconstructing a scene (see Figure 1). The
scene reconstructed using the proposed framework only consists of trustworthy
images (Scene B). Scene B shows that a bulldozer is also involved in the
accident and the accident was not entirely due to the negligence of Bolero’s
driver.

##  3 Related Work

Scene analysis and reconstruction approaches are mostly based on image
processing and object identification [14]. These solutions are usually costly
and computationally intensive [15]. Whereas, existing work on image services
is related to image service selection and composition strategies. A novel
social-sensor cloud service model is proposed in [16]. The proposed approach
conceptualizes the spatio-temporal and textual aspects of social-sensor data
streams as image services’ functional attributes, and the qualitative aspects
as their non-functional attributes. The proposed framework is a 4-stage
algorithm capable of context-aware selection of social-sensor cloud services.
It composes image services to form a mosaic/tapestry in the spatial aspect and
a storyboard in the temporal aspect. An image services composition model is
proposed in [5]. The proposed model composes image services based on user
queries. The model relies only on the meta-data and related posted information
with the image. A heuristics-based social-sensor cloud service selection and
composition model is proposed in [13]. It proposes a context and direction
aware spatio-temporal clustering and recommendation approach. It helps to
compose the relevant image services to form a tapestry in the spatial aspect
and a storyboard in the temporal aspect. A fundamental issue in these
frameworks is that they do not consider trustworthiness of crowdsourced image
services.

Image services in a crowdsourced environment can be untrustworthy. Traditional
approaches to identify untrustworthy images are based on image processing and
machine learning [17]. A multi-modal approach is proposed in [18] that
utilizes new and upgraded models to detect fake images shared over social
media platforms. A deep learning-based approach for detecting fake images is
proposed in [19]. Neural networks and image processing is used in [20] to spot
fake images shared over social media platforms. Traditional digital forensics
and artificial intelligence is used in [21] to identify forgery in images. A
block-based approach for Copy-Move Forgery detection is implemented in [22].
It uses quantized discrete cosine transform coefficients to detect forgery
over an image. Histogram-based fake colorized image detection and feature
encoding-based fake colorized image detection is proposed in [23]. Robust
hashing based fake image detection is proposed in[24]. The aforementioned
image processing based techniques have high accuracy in determining the fakes
in an image but require high computational power and memory.

Some recent studies claim that a subset of trust can be derived using light
weight service-oriented approaches [8, 9, 25]. A new image services trust
model is proposed in [9]. The trustworthiness of an image service is measured
based on the users’ stance. Textual features of the image services, i.e.,
comments and meta-data, e.g., spatio-temporal information are utilized to
gather the trust-rate of the service. A users’ stance and credibility based
image service’s trust model is proposed in [8]. The proposed model considers
various indicators such as the stance embedded in the services’ comments,
their meta-data, e.g., time, along with the users’ credibility. It models the
interactions between commenters and sub-comments in terms of their comments.
Natural Language Processing is leveraged in such solutions to classify text in
a comment [26]. These approaches are unable to capture modifications in an
image service because the misleading content on social media may receive
positive comments from other users [10]. Moreover, comments from credible
users can also be biased [27]. Therefore, we propose a more objective trust
model for image services which also considers modifications and updates in an
image to determine its credibility. The proposed framework is unique in a
sense that it does not require the actual images to be processed to determine
their trustworthiness.

##  4 Image Service Model

We abstract an image as a service having functional and non-functional
attributes. Functional attributes represent the actions involved in capturing
an image.

TABLE I: Description of Metadata Tags Categories |  Description |  Possible Modifications |  Impact | List of Attributes
---|---|---|---|---
Spatial Features |  Spatial metadata tags describe the location at which the image was taken. |  Incorrect location, Fake background |  Forged location tags lead to the selection of spatially incorrect images. | GPS Longitude
GPS Latitude
|  |  |  | GPS Satellite
|  |  |  | City, Country
|  |  |  | Location
|  |  |  | State
Temporal Features |  Temporal metadata tags describe the date and time when the image was taken. |  Outdated images, Manipulations in the objects that reflect time i.e., clock, sun, and moon etc. |  Tampered timestamps lead to the development of a fake story. | Date Digitized
GPS Timezone Offset
|  |  | GPS Timestamp
|  |  |  | GPS Datestamp
|  |  |  | Date Time Original
Contextual Features |  Contextual features define the context of an image. Context of a picture is usually described by textual information available with the image. However, contextual attributes may also contain some other metadata tags i.e., details of the ambience and surroundings. |  Misleading perception consistent with modified objects and modified background |  Fake context may support the existence of fake objects in an image. | Title
Caption
|  |  | Content Description
|  |  | Contribution Description
|  |  |  | Headline
|  |  |  | Image Description
|  |  |  | Keywords
|  |  |  | Instructions
|  |  |  | Weather Profile
|  |  |  | Semantic Names
Image Quality Features |  These metadata tags are related to the quality of an image. |  Cropped Picture, Blurred objects |  Image quality attributes are being modified to either enhance the picture or hide the facts. | Resolution
| Coverage
|  |  |  | White Balance
|  |  |  | Subject Distance
|  |  |  | Camera Elevation Angle

Whereas, non-functional part of an image service can be represented as a
function of spatio-temporal and contextual attributes:

$nf=\\{\zeta,\tau,c,\iota\\}$ (1)

where $ζ$ represents the set of spatial attributes, $τ$ is the set of temporal
attributes, c contains contextual attributes and $ι$ represents intrinsic
attributes.

###  4.1 Non-functional Attributes of an Image Service

We identify different non-functional attributes of an image service that may
indicate changes within an image (see Table I). These metadata tags are
categorized based on the information they contain. This information, if
modified, is an indication of modifications within the image. It is worth
mentioning that some social media platforms remove public access to this
metadata. The proposed framework approach this problem from social media
owners perspective that allows us to assume that we have access to the
metadata. The proposed framework is therefore intended to serve the social
media owners. We group the non-functional attributes into the following two
main categories:

####  4.1.1 Extrinsic Attributes

Extrinsic non-functional attributes usually reflect modifications which are
not within an image i.e., date, time and location etc. Extrinsic non-
functional attributes of an image service can be grouped into the following
subcategories:

Figure 2: Categorization of Potential Modifications in an Image

  * •

Spatial Features: Spatial attributes represent the location where the image
was captured. Location tags help in the selection of spatially relevant images
for a scene reconstruction. Modified spatial tags may be an indication of fake
background in the image [28]. For instance, a footage of a car accident can be
forged to display a fake background. It can make the audience perceive that
the car was at a different location while the accident took place. Changes in
the spatial attributes are denoted by $Δζ$.

  * •

Temporal Features: Temporal attributes represent the date and time when the
image was captured. Date and time have no direct relation with the credibility
of an image. However, it may affect the reconstructed scene because of the
modified timeline [29]. Temporal metadata tags are being forged to develop a
fake story line. For instance, in case of a car accident, timestamp of the
captured scene can be tampered to depict a wrong cause of the accident.
Changes in temporal attributes are represented by $Δτ$.

  * •

Contextual Features: Contextual features are related to the context of an
image. It is often available in form of text or metadata tags. Contextual
features help in the selection and composition of relevant image services
while reconstructing a scene [5]. Fake textual description may support the
fake temporal and spatial tags of an image [6]. For instance, a scene of a car
accident can be described in a way consistent with the fake temporal and
spatial tags. Contextual features may also contain description about the
surroundings. Contextual changes are represented by $Δc$.

####  4.1.2 Intrinsic Attributes

Intrinsic non-functional attributes usually reflect modifications introduced
within an image. For instance, shutter speed and exposure time are two
intrinsic attributes which reflect the proportion of light in an image. These
two attributes are dependent on each other. Higher the shutter speed, the
smaller will be the exposure time [30]. Any discrepancy among these two
attributes may indicate some lighting touch-ups in an image. We consider
changes in spatial, temporal, contextual and intrinsic attributes to formalize
modifications in an image service. Equation 2 can be used to represent changes
in an image service as follows:

$\Delta ImgServ=\\{\Delta\zeta,\Delta\tau,\Delta c,\Delta\iota\\}$ (2)

###  4.2 Categorization of Modifications in Image Services

We consider all those changes in an image that are reflected in one or more
non-functional attributes. In this respect, we propose a categorization that
is based on the nature of changes in the non-functional attributes. The
proposed categorization distinguishes different types of changes that may
require a different treatment to determine the severity of changes. We
categorize the possible modifications in an image into different groups as
shown in Figure 2. These changes in an image service are described as follows:

Figure 3: Possible Extrinsic Changes in Images

Figure 4: Possible Intrinsic Changes in Images

####  4.2.1 Extrinsic Changes

We define extrinsic changes as modifications in an image’s extrinsic non-
functional attributes. Extrinsic changes are not within an image, e.g, changes
in location, timestamp, textual description, etc. The image itself remains
unchanged.

  * •

Date and Time: Temporal attributes of crowdsourced image services may be
modified to depict a different temporal information. It has been observed in
recent years that outdated images are being shared for a recent event. Figure
3a claimed to be two children in 2015 Nepal earthquake. It is actually a
picture of two Vietnamese taken in 2007. The image contains modified temporal
and spatial tags and may mislead the viewer.

  * •

Location: An image can be doctored extrinsically to represent inaccurate
location. The image itself remains unchanged. For instance, Figure 3b was a
viral photo in 2014 claiming the picture of the lost Malaysian MH370 plane. It
turned out to be a photo of a plane crash in New York in 2009.

  * •

Context: Contextual changes may change the perception about a picture. Figure
3c claims a camel with limbs cut off used for begging. The camel is actually
resting with legs bent under itself. The picture is unmodified but described
in a way to support the misleading perception.

####  4.2.2 Intrinsic Changes

We define intrinsic changes as modifications in intrinsic non-functional
attributes reflecting changes within an image. The following types of
intrinsic changes may exist in an image:

  * •

Background: Background of an image can be replaced. Spatial and other non-
functional attributes may reflect changes in the background. Non-functional
attributes may also be modified to make them consistent with the modified
background. Inconsistent spatial attributes may be an indication of a modified
background. Similarly, inconsistencies in image’s quality attributes may also
reflect changes in the background. Figure 4a shows an example photograph with
tampered background.

  * •

Ambience: Introducing clouds, rain, and snow etc., makes an image attractive.
Editing the ambience with good intentions is not supposed to tamper the
relevant metadata tags. These changes are often reflected in the contextual
attributes. Image’s non-functional part can be edited to make it consistent
with the modified ambience of an image. There are many examples of viral
images depicting manipulated disastrous weather. These images may create panic
and unrest among people. Figure 4b is a tailored image showing an exaggerated
scene of Sydney storm in 2018.

  * •

Objects in a Picture: Photo editors tend to remove unnecessary objects from a
picture to improve the background. Figure 4c shows a car being removed from a
photo. Similarly, people have been removed from a scene in Figure 4d. Removal
of objects may be reflected in different intrinsic non-functional attributes.
The removed objects may contain critical information. Removing these objects
may make these pictures impractical for reconstructing a specific scene. An
image may also contain fake objects. Image’s non-functional part can be
modified to make them consistent with the fake objects in an image. There can
be many reasons for the people to add fake objects in an image. For instance,
Figure 4e shows an Iran’s display of military might in 2008. It was doctored
to remove a launcher which failed to fire and replaced with a fourth
projectile.

  * •

Text Arts: Text Arts are widely used while editing a picture. Figure 4f shows
a text placed on a road.

##  5 Proposed Framework

Figure 5: Proposed System Model

A novel framework is proposed in this section to determine the likelihood of
an image to be fake. Figure 5 is a visual demonstration of the proposed
framework.

###  5.1 Computing Semantic Changes in Image Services

This section presents mathematical formalization of image service
modifications. We primarily focus on the extrinsic modifications. The
formalization of intrinsic changes is not covered in this paper. We formalize
the modifications in different attributes of an image service into
mathematical expressions. These expressions are utilized afterwards to
determine the intention of underlying modifications.

Changes in image services can be both semantic (qualitative) and quantitative
in nature. For instance, the changes in an image caption is a semantic change
whereas temporal changes are usually quantitative. In this section, we
represent semantic changes with ‘δ’ and discrete changes with ‘Δ’. In rest of
the sections, ‘Δ’ refers to overall change (both semantic and quantitative) in
an attribute.

####  5.1.1 Semantic Differences

Changes in an image service may change the semantics of an image. To compute
semantic change in an image, we first need to extract the context of an image
and then quantify the change in semantics. We employ LSA in a novel way that
it forms image vectors with the context of an image embedded in the vector
values. In this respect, the attributes are first converted in to image-
attribute matrix ($M_{m\times n}$, where m represents number of images and n
represents number of attributes). We apply standard Single Value Decomposition
(SVD) on image-attribute matrix to find latent topics/contexts in images. It
also reduces the number of columns from m to r (number of latent concepts).

$M_{m\times n}=U_{m\times r}Σ_{r\times r}V^{t}_{r\times n}$ (3)

where U is an $m\times r$ image-aspect matrix with rows representing images
and columns representing latent topics/concepts, $Σ$ is an attribute co-
occurrence matrix with non-negative real numbers on the diagonal, V is an
$r\times n$ attribute-aspect matrix with rows representing attributes and
columns representing latent topics/concepts, and r represents the number of
latent concepts/topics.

Matrix U contains image vectors with the weights of latent topics/concepts
embedded in the vector values. SVD can be applied on spatio-temporal
attributes separately to get two image-aspect matrices. We use the cosine
similarity to quantify the differences in spatio-temporal attributes:

$\delta\overrightarrow{t}=1-\frac{\sum_{1}^{r}\overrightarrow{t_{o}}\overrightarrow{t_{m}}}{\sqrt{\sum_{1}^{r}\overrightarrow{t_{o}^{2}}}\sqrt{\sum_{1}^{r}\overrightarrow{t_{m}^{2}}}}$
(4)

where to represents the original temporal attribute, tm represents the
modified temporal attribute and r is the number of latent topics. Cosine
Similarity can be applied on the spatial attributes in the same way as
follows:

$\delta\overrightarrow{S}=1-\frac{\sum_{1}^{r}\overrightarrow{S_{o}}\overrightarrow{S_{m}}}{\sqrt{\sum_{1}^{r}\overrightarrow{S_{o}^{2}}}\sqrt{\sum_{1}^{r}\overrightarrow{S_{m}^{2}}}}$
(5)

where So represents the original spatial attribute, Sm represents the modified
spatial attribute and r is the number of latent topics. Same notations apply
to contextual attributes.

####  5.1.2 Quantitative Differences

Most of the image service changes are qualitative in nature. However, in some
circumstances where minor changes in spatio-temporal attributes are introduced
to compromise the accuracy, we may need to find the quantitative difference
between attributes. For instance, minor quantitative temporal differences in a
crime scene may impact the evidences. We employ the Manhattan distance metric
to assess disparities among discrete temporal attributes. Calculating
Manhattan distance is computationally less expensive compared to other
distance metrics like Euclidean distance which involves square roots. Previous
studies have demonstrated the efficacy of employing simple anomaly detectors
based on the Manhattan distance metric, particularly in the context of image
data [31]. Another reason of using Manhattan distance is that the discrete
temporal data can be in different units. Moreover, Manhattan distance
considers the difference in each component (year, month, day, hour, etc.)
separately. This aligns with our natural understanding of time where each
component progresses linearly. Another rationale behind using Manhattan
distance is its low sensitivity to extreme values in one component. For
example, a large difference in years won’t heavily outweigh a small difference
in minutes. The following equations shows how we calculate Manhattan distance:

$\Delta t=\sum_{i=1}^{n}|t_{o}[i]-t_{m}[i]|$ (6)

where n represents the number of discrete temporal attributes. It is worth
mentioning that although a pronounced jerk in Manhattan space represents a
high magnitude change but it does not always mean a severe change. Severity of
changes depends on change in semantics of an image.

Spatial attributes can also be modified to compromise on the accuracy. We use
haversine formula to determine the distance between two discrete spatial
attributes because it results in the shortest distance between two points on a
sphere:

$a=\sin^{2}(\Delta\phi/2)+cos\phi_{1}\cdot
cos\phi_{2}\cdot\sin^{2}(\Delta\lambda/2)$ (7)

$c=2\cdot a\tan 2(\sqrt{a},\sqrt{1-a})$ (8)

$d=R\cdot c$ (9)

where $φ$ is latitude, $λ$ is longitude, R is earth’s radius (mean radius =
6,371km), and d represents the distance between two spatial attributes.

Textual attributes are usually qualitative in nature. However, in some cases,
when there are very limited textual attributes are available with an image,
LSA fails to derive context from those attributes. In that cases, we use
Jaccard similarity measure to find the difference in textual attributes:

$\Delta c=\frac{|c_{o}\cap c_{m}|}{|c_{o}\cup c_{m}|}$ (10)

where co is the original text and cm denotes modified text.

Quantitative changes in spatio-temporal and contextual attributes may have
different scales. Therefore, we first normalize the differences to 0-1 range
using the following equations:

$\Delta\hat{\zeta}=\frac{\Delta\zeta_{i}-min(\Delta\zeta,\Delta\tau,\Delta
c)}{max(\Delta\zeta,\Delta\tau,\Delta c)-min(\Delta\zeta,\Delta\tau,\Delta
c)}$ (11)

where $\hat{\zeta}$ represents the normalized value of spatial distance. $Δτ$
and $Δc$ can be normalized similarly.

###  5.2 Intention of Modifications

Every modification in an image may not be a fake. We define ‘fake’ as
modifications that make an image harmful to be used in a specific application.
Fakeness is highly context-sensitive. An image which is fake in one context
may not be fake for another context. For instance, a fake road accident image
may not be possibly fake for other contexts. Therefore, an extensive
investigation of the identified image service modifications is required to
determine the level of fakeness in a modified image. In this respect, we
define intention as a key parameter to determine the likelihood of an image to
be fake. Intention refers to the purpose of introducing modifications in an
image service. Intentions can be ill or well. Therefore, modifications may
either be ill-intentioned or well-intentioned [32]. For instance, improving
the outlook of a picture are usually well-intentioned changes. Well-
intentioned changes do not usually need to be hidden because they are a
positive addition to the image. Whereas, ill-intentioned modifications are
normally concealed from the viewers to hide their evidences. Although, well-
intentioned changes are not supposed to be harmful but they may change the
context of a reconstructed scene. Therefore, all modifications in crowdsourced
image services should be evaluated before reconstructing a scene. We propose a
multi-layer analysis on image service modifications to reach to a conclusion
about image’s legitimacy and fakeness as shown in Figure 5. The proposed
framework considers semantic closeness of the identified modifications and %
obfuscation (attempt to hide the facts) to estimate the intention of changes.

We consider intention as a spectrum to provide an estimate about the
likelihood of a modification to be ill-intentioned or well-intentioned. We
also propose a method to translate intention of modifications into the degree
of fakeness. A change which turns out to be a fake for one scene, may not be
fake for another scene. We propose to estimate intention based on the change
in the semantics of an image service, and % obfuscation. We consider all the
possible combinations of extrinsic modifications ($Δζ$, $Δτ$ and $Δc$) in an
image service to derive intention as shown in Table II.

TABLE II: Deriving the Intention

$Δζ$ | $Δτ$ | $Δc$ | Semantic Change | Intention
---|---|---|---|---
$\downarrow$ | $\downarrow$ | $\downarrow$ | $\downarrow$ | +
$\downarrow$ | $\downarrow$ | $\uparrow$ | $\uparrow$ | -
$\downarrow$ | $\uparrow$ | $\downarrow$ | $\downarrow$ | +
$\downarrow$ | $\uparrow$ | $\downarrow$ | $\uparrow$ | -
. | . | . | . | .
. | . | . | . | .

  * 1

$\downarrow$ refers to a lower value.

  * 2

$\uparrow$ refers to a higher value.

  * 3

\+ refers to inclination towards well-intention.

  * 4

\- refers to inclination towards ill-intention.

Semantic change in Table II represents the overall change in the semantics of
an image. Intention of a few samples is first labeled using fact checked
images. A machine learning algorithm is then trained on these samples which
can be later used to derive the expression for intention. Intention can be
expressed as a function of modifications in an image.

$Intention=\Theta(\Delta\zeta,\Delta\tau,\Delta c)$ (12)

Semantic change is implied in the expression of intention by giving bias to
the changes having higher impact. Weights in the expression of $Θ$ are
computed based on the significance of $Δζ$, $Δτ$ and $Δc$. Precision of
attributes is also taken into consideration while assigning the weights. For
instance, GPS coordinates have high precision as compared to name of a
country. Therefore, GPS coordinates are assigned higher weight. Table III
shows rules to assign weights.

TABLE III: Rules for Assigning Weights

| Significance | Precision | Weights
---|---|---|---
Attributes | High |  $>$90% | 5
High | 80-90% | 4
Medium | 60-70% | 3
Low |  $<$90% | 2
…… | .. | .
…… | .. | .

  * 1

Significance is assumed to have 3 levels: High, medium and low.

  * 2

$Δζ$, $Δτ$ and $Δc$ are assigned weights ranging 1 to 5.

  * 3

A changing threshold concept can be used to define thresholds [33, 34].

Different levels of intention can be defined e.g., extremely ill, ill and well
intention etc. Equation 12 represents a plane in 3d for each value of
intention as shown in Figure 6. Different planes represent different
intensities of intention. Modifications lying close to ill-region represent
the likelihood of being an ill-intention change and vice versa. Integral can
be applied on the equation of each plane to convert it into a region. It helps
in labeling each set of modifications with a value of intention. The following
expression computes the area under the plane.

$IntentionArea=\int\int\Theta d\Delta\zeta d\tau$ (13)

Figure 6: A Planar Region representing Intensity of Intention

####  5.2.1 Finding the Nearest Intention Plane

Intention planes represent the ground truth about intention. We leverage these
planes to estimate the intention of underlying modifications. In this respect,
we find the nearest plane to each set of modifications. We compute the
shortest distance of all modified images from each plane. The plane having the
minimum shortest distance is assigned to the corresponding set of
modifications. The following equation is used to find the shortest distance:

$d=\frac{|a\Delta\zeta+b\Delta\tau+c\Delta c+d|}{\sqrt{a^{2}+b^{2}+c^{2}}}$
(14)

where a, b and c represents the coefficients of the intention.

####  5.2.2 Clustering

Modifications can be clustered before finding nearest intention plane to a
modified image. The nearest intention plane is then found for the whole
cluster which reduces the number of computations required to estimate
intention. We use KMeans clustering to cluster the closely lying
modifications.

####  5.2.3 Proposed Algorithm

The following algorithm provides a pseudo code of the proposed algorithm.

Algorithm 1 : Algorithm to Estimate Intention

1:Input: $ζ$, $τ$, $c$

2:Output: Intention (I)

3:Compute $Δζ$, $Δτ$ and $Δc$

4:Normalize $Δζ$, $Δτ$ and $Δc$

5:Apply binomial regression to predict intention (I) based on modifications
and the overall semantic change

6:Use I to find equation of different intention planes

7:Perform clustering

8:Find shortest distance d of each cluster from every plane

9:return I with minimum distance

####  5.2.4 Translating Intention into Fakeness

The proposed framework provides a detailed analysis on the intention of
modifications to ascertain fake image services. Afterwards, an expert’s
opinion can be leveraged to conclude about the fakeness of a picture.
Alternatively, we can train a machine learning algorithm to translate
intention in to fakeness based on the previous examples. The key difference
between the intention and fakeness is the context. Intention is based on a
more generic context whereas fakeness is determined in a very specific
context. We provide intensity of modifications in each attribute to the
experts or a machine learning algorithm. Experts provide their opinion that
which of the changes may be harmful in a given context along with a confidence
score. Figure 7 shows the supposed analysis provided by the experts.

Figure 7: Translating intention into fakeness

Contextual attributes usually have a wide range of keywords. Repeating the
aforementioned analysis for each word can be cumbersome. Therefore, we employ
TF-IDF (Term Frequency - Inverse Document Frequency) to determine the
significance of modified textual keywords. TF-IDF provides an idea about the
relevancy of the keywords in a specific context. The more relevant a keyword
to a particular context, the more significant that attribute is.

##  6 Experiments and Results

This section explains data acquisition, implementation and results of the
proposed methodology.

###  6.1 Dataset

####  6.1.1 Context-Based Dataset

We evaluate the proposed framework on a real dataset consisting of 1000
images. The images are collected from multiple datasets used in [35]. We also
collect images from different social media platforms (e.g., Twitter, Facebook
and Instagram etc.) and copyright free image repositories (e.g., Shutterstock,
Unsplash and Pexels etc.). The images are collected for 5 different
application domains i.e., road accidents, crime scenes, violent scenes,
natural disasters and public gatherings. We manipulate the metadata of these
images to introduce different types of systematic changes in the images. We
only consider extrinsic non-functional attributes to create these changes.
Hence, the modifications introduced in the images are only extrinsic in nature
as per the scope of this paper. However, there can be a few intrinsic changes
in the images which may be reflected in the extrinsic non-functional
attributes. The changes introduced in the metadata are systematic in nature.
It is ensured that the changes serve a specific purpose. In this respect,
different combinations of changes in spatial, temporal and contextual
attributes are introduced. It is ensured that the changes introduced are both
well-intentioned and ill-intentioned. The original images with metadata serve
as the ground truth in the evaluation process.

####  6.1.2 Image Verification Corpus

We employ another real image metadata dataset for our experiments [36]. This
dataset comprises a diverse collection of images and videos, each accompanied
by its respective metadata. This dataset offers a comprehensive representation
encompassing a wide array of subjects, scenes, and visual attributes. To
extract metadata, we utilize the metadata-extractor library, a Java-based tool
designed for reading metadata from image files. The information extracted
includes details like camera make and model, image dimensions, capture date
and time, GPS coordinates, and other technical specifications. It’s important
to note that while the dataset contains images, our focus is solely on their
associated metadata. We generate various versions of images based on this
metadata dataset leveraging ChatGPT’s proficiency in natural language
generation and contextual comprehension. Furthermore, we employ ChatGPT to
systematically alter the sample metadata of these distinct image versions.
Clear instructions are provided to ChatGPT to induce specific variations in
the metadata of different versions of an image. For example, we introduce
inconsistencies in attributes like shutter speed and exposure time in some
images. Due to its proficient language generation capabilities, ChatGPT can
produce modified metadata, including updated timestamps, adjusted camera
settings, or edited descriptions, signifying that the image has undergone
alterations. As ChatGPT is trained on real world wide web corpus, the
modifications introduced by ChatGPT cannot be regarded completely as synthetic
changes.

Figure 8: Ground Truth of Intention

###  6.2 Implementation

We create a python script to perform LSA on the non-functional attributes of
an image service.

####  6.2.1 Intention of Modifications

We propose a machine-learning-based solution to estimate intention from
previous samples. We adopt the federated learning approach proposed in [37] to
train the model. However, we do not always have the labeled data. In that
case, we can take an expert opinion to label a few training samples. We
leverage state-of-the-art literature to estimate intention for a few samples.
We label different intention score to modified images based on the different
combinations of changes in spatio-temporal and contextual attributes. We then
apply multinomial regression to predict the intention score for the rest of
the dataset. These intention scores represent different intensities of
intention. We define 5 different levels of intention: extremely-ill, very-ill,
moderately-ill, border-line and well-intention. These values form a plane in a
3d Cartesian space for each value of intention. These planes serve as the
ground truth for intention. For the sake of simplicity, we only show one plane
in Figure 8. It shows a plane with the highest intensity of ill-intention
i.e., extremely ill.

####  6.2.2 Clustering

Once we have find out the different intensities of intention in the Cartesian
space, the next step is to plot the data in the same Cartesian space. As
stated earlier, there can be multiple intention regions. The points lying
close to the ill regions will more likely be ill-intentioned changes and vice
versa. We first perform KMeans clustering (as used in [38]) on the combination
of modifications. The intention can then be computed for each cluster rather
than for each individual sample. It reduces the run time complexity of the
algorithm. Figure 9 shows the clusters formed for the modified images in the
context of road accidents.

Figure 9: Shortest Distance of Clusters from Intention Plane

####  6.2.3 Finding the Nearest Intention Plane

Once the modifications have been clustered, the next step is to find the
nearest intention plane to the clusters. We need to find the distance of
clusters from each plane to find the nearest plane. We use equation 14 to find
the distance of clusters from each plane. Figure 9 shows the distance of
clusters from the plane.

####  6.2.4 Translating Intention in to Fakeness

Fakeness is context-sensitive as compared to intention which is context-free.
A change which is ill-intention may not be fake in a specific context.
Intention refers to significance of the combination of overall changes i.e.,
$\Delta\zeta$, $\Delta\tau$ and $\Delta c$. Whereas, fakeness refers to the
severity of changes in a specific context. An insignificant change in
$\Delta\tau$ and $\Delta c$ may still be harmful in a specific context.
Therefore, an intense analysis from the domain expert is required to comment
on the fakeness of the picture. We propose a machine-learning-based solution
to translate intention into fakeness depending upon the changes in individual
attributes along with their intention. In that respect, we label fakeness
score for few samples which serves as the training set. We train a machine
learning model using a python script. It return the fakeness score for each
set of non-functional attributes i.e., $\Delta\tau$ and $\Delta c$.

###  6.3 Results

We test our methodology on the three metrics: accuracy, precision and run-time
efficiency.

####  6.3.1 Accuracy

As mentioned earlier, there will be multiple intention planes in the 3d
Cartesian space representing different intensities of intention. The accuracy
is computed as the percentage of samples closest to the correct intention
plane they belong to. We test the accuracy of the proposed methodology for 2,
3 and 4 planes. We can choose any number of intention planes while training
the model. We test our proposed clustering with bruteforce and basic
heuristics. Heuristics-based approach don’t perform clustering but try to
minimize the number of iterations required to find the nearest plane. Figure
10 shows the percentage accuracy of these approaches in different scenarios
tested on the context-based dataset. Bruteforce approach shows 89% accuracy
with 4 planes. Whereas, heuristics and clustering show 83% and 78% accuracy
respectively. We also test the accuracy of the proposed approach on image
verification corpus (refer to Figure 11). The accuracy drops slightly on this
dataset. The reason behind this drop in accuracy could be the unavailability
of substantial metadata tags with each image. Overall, the results indicate
that the brute force has the best accuracy. The drop in accuracy may be
attributed to the limited availability of substantial metadata tags associated
with each image. Overall, The accuracy increases with the increase in the
number of intention planes.

Figure 10: Accuracy of the Proposed Approach on Context-Based Dataset

Figure 11: Accuracy of the Proposed Approach on Image Verification Corpus

Figure 12 shows the increase in accuracy with the increase in intention planes
for both datasets. It can be noted that the rate of increase in accuracy
decreases after increasing the vector size up to a certain extent.

Figure 12: Accuracy of the Proposed Approach

####  6.3.2 Precision

We use precision to report the correctly identified ill-intentioned changes.
Figure 13 shows the precision of the proposed approach for different number of
intention planes. Precision increases with the increase in number of planes.

Figure 13: Precision of Detecting Ill-intentioned Changes

####  6.3.3 Run-Time Efficiency

We report the run-time efficiency of the proposed approach in terms of time
consumed by the algorithm and run-time complexity to train the algorithms.
Run-time is reported in nanoseconds (a SI unit of time equal to $10^{-9}$
seconds). Table IV shows the run-times of different approaches for both
datasets. The framework takes slightly more time for image verification corpus
because of data cleaning and preprocessing. Clustering is the most efficient
approach in terms of run-time efficiency. Whereas, the overhead of training in
clustering is O(N2). Heuristics-based approach has a better run-time
efficiency as compared to baseline whereas, its run-time complexity is O(N).

TABLE IV: Run Time Efficiency Time consumed (ns) | Brute-force | Heuristics | Clustering
---|---|---|---
Context-Based Dataset | 28000 | 14000 | 1010
Image Verification Corpus | 31000 | 15400 | 1125
Run-time complexity | 1 | O(N) |  O($N^{2}$)

####  6.3.4 Comparison with State-of-the-Art

We compare the performance of the proposed approach with a state-of-the-art.
As stated earlier, our proposed approach is based on Latent Semantic Analysis
(LSA) to derive semantics of the non-functional attributes of an image
service. We compare the performance of our LSA-based framework with a recently
proposed approach named as TFIDF-FastText [39]. TFIDF-FastText classifies
semantically similar short texts. It uses a fusion word-embedding technique –
TF-IDF weighted average FastText, to explore the lexical and semantic features
of the text. It results in deriving quantitative semantic representation of
word tokens. TF-IDF can capture the most descriptive words in a sentence which
helps in the efficient clustering of text into classes. The FastText model
facilitates the representation of text in a continuous, low-dimensional space,
and can record the semantics of a text. Figure 14 provides a comparison of the
accuracy achieved by the proposed approach and the state-of-the-art. TF-IDF
achieves 39% accuracy. However, fusing TFIDF with FastText increases the
accuracy up to 58%. Whereas, the proposed LSA-based approach outperforms
TFIDF-FastText achieving an accuracy of 78%. LSA is more effective as it
identifies the latent semantic structure in an image metadata. Whereas, TF-IDF
is based on the frequency of words.

Figure 14: Comparison with the state-of-the-art

##  7 Conclusion

A novel framework is proposed in this paper to ascertain fake crowdsourced
images based on the changes in image metadata. An image is abstracted as a
service. The proposed approach is unique in the sense that it relies
completely on the changes in image’s non-functional attributes, i.e., image
metadata, to determine the trustworthiness of crowdsourced image services.
Intention is defined as an important parameter to reflect the likelihood of an
image being fake. A novel framework is proposed to estimate the intention of
underlying modifications. A clustering approach is then used to determine the
likelihood of an image being a fake. The results show high accuracy and run-
time efficiency.

## Acknowledgment

This research was partly made possible by LE220100078 and DP220101823 grants
from the Australian Research Council. The statements made herein are solely
the responsibility of the authors.

## References

  * [1] H. M. Griffis, A. S. Kilaru, R. M. Werner, D. A. Asch, J. C. Hershey, S. Hill, Y. P. Ha, A. Sellers, K. Mahoney, and R. M. Merchant, “Use of social media across us hospitals: descriptive analysis of adoption and utilization,” _Journal of medical internet research_ , vol. 16, no. 11, p. e264, 2014.
  * [2] X. Liu, R. Troncy, and B. Huet, “Using social media to identify events,” in _Proceedings of the 3rd ACM SIGMM international workshop on Social media_ , 2011, pp. 3–8.
  * [3] A. Rosi, M. Mamei, F. Zambonelli, S. Dobson, G. Stevenson, and J. Ye, “Social sensors and pervasive services: Approaches and perspectives,” in _2011 IEEE international conference on pervasive computing and communications workshops (PERCOM Workshops)_. IEEE, 2011, pp. 525–530.
  * [4] Y. Gu, Z. S. Qian, and F. Chen, “From twitter to detector: Real-time traffic incident detection using social media data,” _Transportation research part C: emerging technologies_ , vol. 67, pp. 321–342, 2016.
  * [5] T. Aamir, H. Dong, and A. Bouguettaya, “Social-sensor composition for scene analysis,” in _International Conference on Service-Oriented Computing_. Springer, 2018, pp. 352–362.
  * [6] C. Shen, M. Kasra, W. Pan, G. A. Bassett, Y. Malloch, and J. F. O’Brien, “Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online,” _New media & society_, vol. 21, no. 2, pp. 438–463, 2019.
  * [7] A. Gupta, H. Lamba, P. Kumaraguru, and A. Joshi, “Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy,” in _Proceedings of the 22nd international conference on World Wide Web_ , 2013, pp. 729–736.
  * [8] T. Aamir, H. Dong, and A. Bouguettaya, “Stance and credibility based trust in social-sensor cloud services,” in _International Conference on Web Information Systems Engineering_. Springer, 2018, pp. 178–189.
  * [9] T. Aamir et al, “Trust in social-sensor cloud service,” in _2018 IEEE International Conference on Web Services (ICWS)_. IEEE, 2018, pp. 359–362.
  * [10] J. Colliander, ““this is fake news”: Investigating the role of conformity to other users’ views when commenting on and spreading disinformation in social media,” _Computers in Human Behavior_ , vol. 97, pp. 202–215, 2019.
  * [11] P. Ghosh, V. Morariu, B.-C. I. Larry Davis _et al._ , “Detection of metadata tampering through discrepancy between image content and metadata using multi-task deep learning,” in _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops_ , 2017, pp. 60–68.
  * [12] C. Riggs, T. Douglas, and K. Gagneja, “Image mapping through metadata,” in _2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC)_. IEEE, 2018, pp. 1–8.
  * [13] T. Aamir, H. Dong, and A. Bouguettaya, “Social-sensor composition for tapestry scenes,” _IEEE Transactions on Services Computing_ , 2020.
  * [14] J. Chae, D. Thom, H. Bosch, Y. Jang, R. Maciejewski, D. S. Ebert, and T. Ertl, “Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition,” in _2012 IEEE Conference on Visual Analytics Science and Technology (VAST)_. IEEE, 2012, pp. 143–152.
  * [15] G. Slabaugh, R. Schafer, T. Malzbender, and B. Culbertson, “A survey of methods for volumetric scene reconstruction from photographs,” in _Volume Graphics 2001_. Springer, 2001, pp. 81–100.
  * [16] T. Aamir, A. Bouguettaya, H. Dong, S. Mistry, and A. Erradi, “Social-sensor cloud service for scene reconstruction,” in _International Conference on Service-Oriented Computing_. Springer, 2017, pp. 37–52.
  * [17] M. Patel, J. Padiya, and M. Singh, “Fake news detection using machine learning and natural language processing,” in _Combating Fake News with Computational Intelligence Techniques_. Springer, 2022, pp. 127–148.
  * [18] B. Singh and D. K. Sharma, “Predicting image credibility in fake news over social media using multi-modal approach,” _Neural Computing and Applications_ , pp. 1–15, 2021.
  * [19] C.-C. Hsu, Y.-X. Zhuang, and C.-Y. Lee, “Deep fake image detection based on pairwise learning,” _Applied Sciences_ , vol. 10, no. 1, p. 370, 2020.
  * [20] B. Singh and D. K. Sharma, “Image forgery over social media platforms-a deep learning approach for its detection and localization,” in _2021 8th International Conference on Computing for Sustainable Global Development (INDIACom)_. IEEE, 2021, pp. 705–709.
  * [21] J. Yang, S. Xiao, A. Li, G. Lan, and H. Wang, “Detecting fake images by identifying potential texture difference,” _Future Generation Computer Systems_ , vol. 125, pp. 127–135, 2021.
  * [22] V. Kour, P. Aggarwal, and R. Kaur, “A fast block-based technique to detect copy-move forgery in digital images,” in _Recent Advances in Artificial Intelligence and Data Engineering_. Springer, 2022, pp. 299–307.
  * [23] Y. Guo, X. Cao, W. Zhang, and R. Wang, “Fake colorized image detection,” _IEEE Transactions on Information Forensics and Security_ , vol. 13, no. 8, pp. 1932–1944, 2018.
  * [24] M. Tanaka and H. Kiya, “Fake-image detection with robust hashing,” in _2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech)_. IEEE, 2021, pp. 40–43.
  * [25] M. Umair, A. Bouguettaya, and A. Lakhdari, “Detecting changes in crowdsourced social media images,” _arXiv preprint arXiv:2310.16848_ , 2023.
  * [26] M. Umair, Z. Saeed, M. Ahmad, H. Amir, B. Akmal, and N. Ahmad, “Multi-class classification of bi-lingual sms using naive bayes algorithm,” in _2020 IEEE 23rd International Multitopic Conference (INMIC)_. IEEE, 2020, pp. 1–5.
  * [27] F. Zimmer, K. Scheibe, M. Stock, and W. G. Stock, “Fake news in social media: Bad algorithms or biased users?” _Journal of Information Science Theory and Practice_ , vol. 7, no. 2, pp. 40–53, 2019.
  * [28] M. Elmaci, A. N. Toprak, and V. Aslantas, “A comparative study on the detection of image forgery of tampered background or foreground,” in _2021 9th International Symposium on Digital Forensics and Security (ISDFS)_. IEEE, 2021, pp. 1–5.
  * [29] R. Padilha, T. Salem, S. Workman, F. A. Andaló, A. Rocha, and N. Jacobs, “Content-based detection of temporal metadata manipulation,” _arXiv preprint arXiv:2103.04736_ , 2021.
  * [30] G. Simon, M. Rátosi, and G. Vakulya, “Automatic measurement of digital cameras’ exposure time using equivalent sampling,” _IEEE Transactions on Instrumentation and Measurement_ , vol. 71, pp. 1–10, 2022.
  * [31] L. Li, W. Li, Q. Du, and R. Tao, “Low-rank and sparse decomposition with mixture of gaussian for hyperspectral anomaly detection,” _IEEE Transactions on Cybernetics_ , vol. 51, no. 9, pp. 4363–4372, 2020.
  * [32] E. Lowe-Calverley and R. Grieve, “Self-ie love: Predictors of image editing intentions on facebook,” _Telematics and Informatics_ , vol. 35, no. 1, pp. 186–194, 2018.
  * [33] M. Umair, M. A. Cheema, B. Afzal, and G. Shah, “Energy management of smart homes over fog-based iot architecture,” _Sustainable Computing: Informatics and Systems_ , vol. 39, p. 100898, 2023.
  * [34] M. Umair and G. A. Shah, “Energy management of smart homes,” in _2020 IEEE International Conference on Smart Computing (SMARTCOMP)_. IEEE, 2020, pp. 247–249.
  * [35] Detection and visualization of misleading content on Twitter, “Boididou, christina and papadopoulos, symeon and zampoglou, markos and apostolidis, lazaros and papadopoulou, olga and kompatsiaris, yiannis,” _International Journal of Multimedia Information Retrieval_ , vol. 7, no. 1, pp. 71–86, 2018.
  * [36] D. Noakes, “Database of images from various digital cameras,” https://github.com/drewnoakes/metadata-extractor-images, 2019.
  * [37] J. Akram, M. Umair, R. H. Jhaveri, M. N. Riaz, H. Chi, and S. Malebary, “Chained-drones: Blockchain-based privacy-preserving framework for secure and intelligent service provisioning in internet of drone things,” _Computers and Electrical Engineering_ , vol. 110, p. 108772, 2023.
  * [38] S. A. Rizvi, M. Umair, and M. A. Cheema, “Clustering of countries for covid-19 cases based on disease prevalence, health systems and environmental indicators,” _Chaos, Solitons & Fractals_, vol. 151, p. 111240, 2021.
  * [39] S. Chawla, R. Kaur, and P. Aggarwal, “Text classification framework for short text based on tfidf-fasttext,” _Multimedia Tools and Applications_ , pp. 1–14, 2023.

|  Muhammad Umair is a PhD student at the School of Computer Science, The
University of Sydney. He completed his B.Sc. Electrical Engineering and M.Sc.
Electrical Engineering from University of Engineering & Technology (UET)
Lahore in 2014 and 2017, respectively. He has worked as a Research Officer at
Internet of Things (IoT) lab at Al-Khwarizmi Institute of Computer Sciences,
UET Lahore. He has also worked at Sultan Qaboos IT Research lab as a Research
Officer.
---|---
|  Athman Bouguettaya is a Professor in the School of Computer Science at the
University of Sydney. He received his Ph.D. in Computer Science from the
University of Colorado at Boulder (USA) in 1992. He is or has been on the
editorial boards of several journals, including the IEEE Transactions on
Services Computing, ACM Transactions on Internet Technology, the International
Journal on Next Generation Computing, and VLDB Journal. He is a Fellow of the
IEEE and a Distinguished Scientist of the ACM.
---|---
|  Abdallah Lakhdari is a Postdoctoral Fellow in the School of Computer
Science at the University of Sydney. He received his Bachelor’s degree (2010)
and Master’s degree (2013) in Computer Science from The University of
Laghouat, Algeria. He was a visiting scholar a New Mexico Tech. He worked as a
lecturer at the Department of Computer Science at The University of Laghouat,
Algeria. His research interests include Social Computing, Crowdsourcing, IoT,
and Service Computing.
---|---