Original: Service-based Analysis and Abstraction for Content Moderation of Digital Images
Revised: Service-based Analysis and Abstraction for Content Moderation of Digital Images

Original: Online Submission ID:

Original: A BSTRACT
Revised: A BSTRACT

Original: This paper presents a service-based approach towards content mod- eration of digital visual media while browsing web pages.
Revised: This paper presents a service-based approach towards content mod- eration of digital visual media while browsing web pages.

Original: It enables the automatic analysis and classiﬁcation of possibly offensive con- tent, such as images of violence, nudity, or surgery, and applies common image abstraction techniques at different levels of abstraction to these to lower their affective impact.
Revised: It enables the automatic analysis and classiﬁcation of possibly offensive con- tent, such as images of violence, nudity, or surgery, and applies common image abstraction techniques at different levels of abstraction to these to lower their affective impact.

Original: The system is implemented using a microservice architecture that is accessible via a browser ex- tension, which can be installed in most modern web browsers.
Revised: The system is implemented using a microservice architecture that is accessible via a browser ex- tension, which can be installed in most modern web browsers.

Original: It can be used to facilitate content moderation of digital visual media such as digital images or to enable parental control for child protection.
Revised: It can be used to facilitate content moderation of digital visual media such as digital images or to enable parental control for child protection.

Original: 1 I NTRODUCTION
Revised: 1 I NTRODUCTION

Original: This work’s main objective is to support and facilitate human-driven1 moderation of digital visual media such as digital images, which area dominant category in the domain of user-generated content, i.e., 3 content that is acquired and uploaded to content providers.
Revised: This work’s main objective is to support and facilitate human-driven moderation of digital visual media such as digital images, which are a dominant category in the domain of user-generated content, i e., content that is acquired and uploaded to content providers.

Original: Contentmoderation usually requires humans who view and decide if content5 is considered to be appropriate or not, e.g., regarding violence,racism, nudity, privacy etc.; these aspects may have serious impacts7 in various directions, including legal and psychological issues.
Revised: Content moderation usually requires humans who view and decide if content is considered to be appropriate or not, e.g., regarding violence, racism, nudity, privacy etc.; these aspects may have serious impacts in various directions, including legal and psychological issues.

Original: To cope with negative impacts on the viewers psychology and to9 alleviate legal issues, we propose a combination of content analysisand classiﬁcation together with suitable image abstraction techniques11 that ﬁrst detect inappropriate content and, subsequently, disguiseand obfuscate content depictions or speciﬁc portions (Fig. 1).
Revised: To cope with negative impacts on the viewers’ psychology and to alleviate legal issues we propose a combination of content analysis and classiﬁcation together with suitable image abstraction techniques that ﬁrst detect inappropriate content and, subsequently, disguise and obfuscate content depictions or speciﬁc portions (Fig. 1).

Original: 1.1 Problem Statement and Objectives
Revised: 1.1 Problem Statement and Objectives

Original: From a technical perspective, the implementation of such approach15 needs to be independent of operation systems and processing hardware.
Revised: From a technical perspective, the implementation of such approach needs to be independent of operation systems and processing hardware.

Original: Thus, we decide to use a service-based approach to detect17 and classify visual media content and to perform respective abstraction techniques depending on the detection results.
Revised: Thus, we decide to use a service-based approach to detect and classify visual media content and to perform respective abstraction techniques depending on the detection results.

Original: Deep-learning19 approaches will be used that allow for deﬁning what “offensive”means.
Revised: Deep-learning approaches will be used that allow for deﬁning what “offensive” means.

Original: Such functionality can be integrated into web browsers using21 a browser-extension based on a World Wide Web Consortium (W3C)draft standard.
Revised: Such functionality can be integrated into web browsers using a browser-extension based on a World Wide Web Consortium (W3C) draft standard.

Original: This way, the content moderation functionality can23 be applied and integrated into professional IT solutions or can beused by means of end-user apps.
Revised: This way, the content moderation functionality can be applied and integrated into professional IT solutions or can be used by means of end-user apps.

Original: Facilitate Content Moderation (Objective-1):
Revised: Facilitate Content Moderation (Objective-1):

Original: Today, con-26 tent moderation becomes more and more crucial for digital contentproviders (e.g., Facebook or YouTube) to fulﬁll their responsibili-28 ties and to implement an ethical content handling [7].
Revised: Today, con- tent moderation becomes more and more crucial for digital content providers (e.g., Facebook or YouTube) to fulﬁll their responsibili- ties and to implement an ethical content handling [9].

Original: Moderationcomprises manual examination for detection and classiﬁcation of30 critical or inappropriate content.
Revised: Moderation comprises manual examination for detection and classiﬁcation of critical or inappropriate content.

Original: For some types of visual mediacontent, the detection and classiﬁcation can already be performed[Figure] [Figure] semi-automatically using machine-learning approaches.
Revised: For some types of visual media content, the detection and classiﬁcation can already be performed semi-automatically using machine-learning approaches.

Original: However,1 automated moderation being often limited (see e.g., [13]), the ﬁnal moderation decisions are often made by humans, required to3 consume the unﬁltered content.
Revised: However, automated moderation being often limited (see e.g., [15]), the ﬁ- nal moderation decisions are often made by humans, required to consume the unﬁltered content.

Original: Recent studies indicate that workers concerned with these tasks5 are often subject to severe mental health damage due to traumaticexperiences or monotonic duties [10, 11, 29].
Revised: Recent studies indicate that workers concerned with these tasks are often subject to severe mental health damage due to traumatic experiences or monotonic duties [12,13,31].

Original: Interestingly, non- 7 photo-realistic rendering of these stimuli could potentially reducetheir negative impacts [1,2,12].
Revised: Interestingly, non-photo- realistic rendering of these stimuli has shown promising results in reducing their negative impacts [1–3,14,39].

Revised: Further assessments of the effectiveness of such image processing techniques fall beyond the scope of this manuscript, which aims at providing a service-based implementation of what past literature has identiﬁed as helpful in mitigating the impact of shocking media.

Original: Therefore, these negative conse- 9 quences could be mitigated by reducing the affective responses thatarise from consuming the unﬁltered content by using a combination11 of automatic analysis and abstraction techniques as follow (Fig. 2): ( i ) visual media content is analyzed, e.g., to detect, classify, and pos- 13 sibly perform a semantic segmentation, ( ii ) abstraction techniquesare used to partially or completely disguise possibly offensive con- 15 tent prior to ( iii ), the interactive visual examination.
Revised: Therefore, these neg- ative consequences could be mitigated by reducing the affective responses that arise from consuming the unﬁltered content by using a combination of automatic analysis and abstraction techniques as follow (Fig. 2): ( i ) visual media content is analyzed, e.g., to detect, classify, and possibly perform a semantic segmentation, ( ii ) abstraction techniques are used to partially or completely disguise possibly offensive content prior to ( iii ), the interactive visual examination.

Original: Service-oriented Architectures (Objective-2): To implement17 an approach for Objective-1 and to make it available to a wide range of applications and users, we set out to provide a prototypi-19 cal micro-app (e g., a browser extension) based on a microservice 
Revised: Service-oriented Architectures (Objective-2): To implement an approach for Objective-1 and to make it available to a wide range of applications and users, we set out to provide a prototypi- cal micro-app (e.g., a browser extension) based on a microservice infrastructure.

Original: infrastructure.

Original: For this, two separate microservices, for analysis and abstraction respectively, are orchestrated by a content moderator
Revised: For this, two separate microservices, for analysis and abstraction respectively, are orchestrated by a content moderator

Revised: [Figure]

Revised: [Figure]

Original: service.
Revised: service.

Original: This enables a use-case speciﬁc exchange, replacement, or extension of speciﬁc functionality or complete services without risking the overall functionality.
Revised: This enables a use-case speciﬁc exchange, replacement, or extension of speciﬁc functionality or complete services without risking the overall functionality.

Original: By using a Hyper Text Markup Lan-25 guage (HTML)
Revised: By using a Hyper Text Markup Lan- guage (HTML)

Original: User Interface (UI) integrated into a W3C browser extension, the abstracted content can be interpolated/blended with 
Revised: User Interface (UI) integrated into a W3C browser extension, the abstracted content can be interpolated/blended with the original one interactively.

Original: the original one interactively

Original: In current state-of-the-art systems, analysis and abstraction of29 images and videos are mostly performed using on-device computation.
Revised: In current state-of-the-art systems, analysis and abstraction of images and videos are mostly performed using on-device computation.

Original: Thus, these systems’ processing capabilities are limited by31 the device’s hardware (Central Processing Unit (CPU) and Graphics Processing Unit (GPU)) and software (Operating System (OS)).
Revised: Thus, these systems’ processing capabilities are limited by the device’s hardware (Central Processing Unit (CPU) and Graphics Processing Unit (GPU)) and software (Operating System (OS)).

Original: 33 Being subject to high heterogeneity (device ecosystem), this hasmajor drawbacks concerning the applicability, maintainability, and35 provisioning of content-moderation applications, in particular: asoftware development process and integration into 3 rd -party applications is aggravated by: ( i ) different operating systems (e.g., Win-38 dows, Linux, macOS, iOS, Android), ( ii ) heterogeneous hardwareconﬁgurations of varying efﬁciency and Application Programming40 Interfaces (APIs) (e.g., OpenGL, Vulkan, Metal, DirectX), as wellas ( iii ) display sizes and formats.
Revised: Being subject to high heterogeneity (device ecosystem), this has major drawbacks concerning the applicability, maintainability, and provisioning of content-moderation applications, in particular: a software development process and integration into 3 rd -party applications is aggravated by: ( i ) different operating systems (e.g., Win- dows, Linux, macOS, iOS, Android), ( ii ) heterogeneous hardware conﬁgurations of varying efﬁciency and Application Programming Interfaces (APIs) (e.g., OpenGL, Vulkan, Metal, DirectX), as well as ( iii ) display sizes and formats.

Original: Further, on-device processing does42 often not scale with respect to the increasing input complexity (e.g.,number of images, increasing spatial resolution of camera sensors),44 which especially poses problems for mobile devices (e.g., batterydrain or overheating).
Revised: Further, on-device processing does often not scale with respect to the increasing input complexity (e.g., number of images, increasing spatial resolution of camera sensors), which especially poses problems for mobile devices (e.g., battery drain or overheating).

Original: 1.2 Approach and Contributions
Revised: 1.2 Approach and Contributions

Original: Concerning the aforementioned drawbacks of on-device processing,48 the proposed combination of standardized technology for micro-appstogether with service-oriented architectures and infrastructures offers1 a variety of advantages, in particular: ( i ) implementation and testing2 of speciﬁc analysis and abstraction techniques are required to be3 performed only once (controlling the systems software and hardware4 due to virtualization), ( ii ) functionality is offered to a wide range5 of web-based applications using standardized protocols, which can6 be easily integrated into 3 rd -party applications and extended rapidly.
Revised: Concerning the aforementioned drawbacks of on-device processing, the proposed combination of standardized technology for micro-apps together with service-oriented architectures and infrastructures offers a variety of advantages, in particular: ( i ) implementation and testing of speciﬁc analysis and abstraction techniques are required to be performed only once (controlling the systems software and hardware due to virtualization), ( ii ) functionality is offered to a wide range of web-based applications using standardized protocols, which can be easily integrated into 3 rd -party applications and extended rapidly.

Original: 7 This way, ( iii ) the proposed service-based approach can automati8 cally scale service-instances with respect to input data complexity9 and computing power required.
Revised: This way, ( iii ) the proposed service-based approach can automati- cally scale service-instances with respect to input data complexity and computing power required.

Original: Thus software up-to-dateness and10 exchangeability can be easily achieved.
Revised: Thus software up-to-dateness and exchangeability can be easily achieved.

Original: Further, the software devel11 opment process of web-based thin-clients is less complex compared12 to rich-clients.
Revised: Further, the software development process of web-based thin-clients is less complex compared to rich-clients.

Original: Together with the upcoming 5G telecommunication13 standard featuring (among others) high data rates, reduced latency,14 and energy saving, the presented approach seems feasible to be ap15 plied in stationary as well as mobile contexts.
Revised: Together with the upcoming 5G telecommunication standard featuring (among others) high data rates, reduced latency, and energy saving, the presented approach seems feasible to be applied in stationary as well as mobile contexts.

Original: Finally, intellectual16 property of the service providers can be effectively protected by not17 shipping respective software components to customers, and thus,18 impede the possibility of reverse engineering.
Revised: Finally, intellectual property of the service providers can be effectively protected by not shipping respective software components to customers, and thus, impede the possibility of reverse engineering.

Original: 2 B ACKGROUND AND R ELATED W ORK 
Revised: 2 B ACKGROUND AND R ELATED W ORK 2.1 Visual Content Analysis using Neural Networks

Original: 2.1 Visual Content Analysis using Neural Networks

Original: Image analysis can be performed according to different tasks.
Revised: Image analysis can be performed according to different tasks.

Original: Im22 age classiﬁcation such as the ResNet Convolutional Neural Net23 work (CNN) architecture can be performed [9] to determine how24 likely an image belongs to one or more speciﬁc categories.
Revised: Im- age classiﬁcation such as the ResNet Convolutional Neural Network (CNN) architecture can be performed [11] to determine how likely an image belongs to one or more speciﬁc categories.

Original: With25 object recognition, the goal is to identify objects displayed on im26 ages with it respective bounding boxes.
Revised: With object recognition, the goal is to identify objects displayed on im- ages with it respective bounding boxes.

Original: For object recognition,27 CNN architectures such as is YOLOv3 [23] and Single Shot Detec28 tor (SSD)
Revised: For object recognition, CNN architectures such as is YOLOv3 [25] and Single Shot Detec- tor (SSD)

Original: [17] can be applied.
Revised: [19] can be applied.

Original: Another task is image segmentation,29 where objects and regions of certain semantics are identiﬁed on a30 per-pixel base.
Revised: Another task is image segmentation, where objects and regions of certain semantics are identiﬁed on a per-pixel base.

Original: This can be performed with the approach R-CNN31 of Girshick et al .
Revised: This can be performed with the approach R-CNN of Girshick et al .

Original: Different kinds of CNNs need to be trained32 with datasets consisting of images labeled with categories, objects,33 or image regions.
Revised: Different kinds of CNNs need to be trained with datasets consisting of images labeled with categories, objects, or image regions.

Original: There are public datasets available to train and to34 benchmark the performance of different CNN architectures.
Revised: There are public datasets available to train and to benchmark the performance of different CNN architectures.

Original: Popular35 examples are the Pascal VOC [4] and ADE20K [38] datasets.
Revised: Popular examples are the Pascal VOC [6] and ADE20K [40] datasets.

Original: They36 contain labeled image data for image classiﬁcation, object recog37 nition,and even semantic segmentation.
Revised: They contain labeled image data for image classiﬁcation, [Figure] nition,

Original: For the task of content 38 moderation, the objects and categories are quite general; mostly39 everyday objects are contained.
Revised: [Figure] moderation, the objects and categories are quite general; mostly everyday objects are contained.

Original: Another dataset is the Google Open40 Image dataset [14].
Revised: Another dataset is the Google Open Image dataset [16].

Original: It contains about 9 million images labeled with41 object information.
Revised: It contains about 9 million images labeled with object information.

Original: The object categories are hierarchical organized42 and span over 6000 categories.
Revised: The object categories are hierarchical organized and span over 6000 categories.

Original: Another approach, which spans even43 more different object categories, is YOLO

Revised: Another approach, which spans even more different object categories, is YOLO

Original: YOLO9000 is44 a variation of the YOLOv3 [23] architecture that was trained on a45 dataset with more than 9000 different objects.
Revised: YOLO9000 is a variation of the YOLOv3 [25] architecture that was trained on a dataset with more than 9000 different objects.

Original: However, this dataset46 is not publicly available.
Revised: However, this dataset is not publicly available.

Original: 47 Further, there are approaches for image analysis that are more48 directed towards content moderation.
Revised: Further, there are approaches for image analysis that are more directed towards content moderation.

Original: These are mostly presented in49 the form of RESTful APIs allowing for sending images and receive50 analysis results.
Revised: These are mostly presented in the form of RESTful APIs allowing for sending images and receive analysis results.

Original: The Google Cloud Vision API [26] assigns scores to51 images depending on how likely they represent the categories adult ,52 violence , medical and spoof .
Revised: The Google Cloud Vision API [28] assigns scores to images depending on how likely they represent the categories adult , violence , medical and spoof .

Original: The API of Sightengine [28] analyzes53 images for the occurrence of categories such as nudity , weapon , alco54 hol , drugs , scams and other offensive content.
Revised: The API of Sightengine [30] analyzes images for the occurrence of categories such as nudity , weapon , alcohol , drugs , scams and other offensive content.

Original: Valossa [18] reviews55 cloud-based vendors supporting the classiﬁcation of unsafe content56 and describes the difﬁculties of deﬁning what inappropriate content57 actually is.
Revised: Valossa [20] reviews cloud-based vendors supporting the classiﬁcation of unsafe content and describes the difﬁculties of deﬁning what inappropriate content actually is.

Original: They concluded that content analysis models must be58 able to understand the context of objects and depicted situations in59 order to decide whether images contain unsafe content.
Revised: They concluded that content analysis models must be able to understand the context of objects and depicted situations in order to decide whether images contain unsafe content.

Original: They offer an60 evaluation dataset with images in 16 different content categories and61 benchmark it on several online RESTful APIs.
Revised: They offer an evaluation dataset with images in 16 different content categories and benchmark it on several online RESTful APIs.

Original: However, these ap62 proaches do not offer any public datasets or specify which machine63 learning models they use exactly.
Revised: How- ever, these approaches do not offer any public datasets or specify which machine learning models they use exactly.

Original: In contrast, Yahoo [19] offers a64 trained CNN model that can be used free of charge.
Revised: In contrast, Ya- hoo [21] offers a trained CNN model that can be used free of charge.

Original: The Yahoo Open Not Safe For Work (NSFW) model is basically a ResNet [9] 1 that was ﬁne-tuned on a dataset of NSFW images depicting nudityand other offensive content.
Revised: The Yahoo Open Not Safe For Work (NSFW) model is basically a ResNet [11] that was ﬁne-tuned on a dataset of NSFW images depicting nudity and other offensive content.

Original: For a given image, it determines a score3 how likely it contains unsafe content.
Revised: For a given image, it determines a score how likely it contains unsafe content.

Original: 2.2 Service-based Image Processing
Revised: 2.2 Service-based Image Processing

Original: Several software architectural patterns are feasible to implement6 service-based image processing.
Revised: Several software architectural patterns are feasible to implement service-based image processing.

Original: However, one prominent style ofbuilding a web-based processing system for any data is the service-8 oriented architecture [31].
Revised: However, one prominent style of building a web-based processing system for any data is the service- oriented architecture [33].

Original: It enables server developers to set upvarious processing endpoints, each providing a speciﬁc functionality10 and covering a different use case.
Revised: It enables server developers to set up various processing endpoints, each providing a speciﬁc functionality and covering a different use case.

Original: These endpoints are accessibleas a single entity to the client, i.e., the implementation is hidden for12 the requesting clients, but can be implemented through an arbitrarynumber of self-contained services.
Revised: These endpoints are accessible as a single entity to the client, i.e., the implementation is hidden for the requesting clients, but can be implemented through an arbitrary number of self-contained services.

Original: Since web services are usually designed to maximize their15 reusability, their functionality should be simple and atomic.
Revised: Since web services are usually designed to maximize their reusability, their functionality should be simple and atomic.

Original: Therefore, the composition of services is critical for fulﬁlling more com- 17 plex use cases [15].
Revised: There- fore, the composition of services is critical for fulﬁlling more complex use cases [17].

Original: The two most prominent patterns for implementing such composition are choreography and orchestration.
Revised: The two most prominent patterns for imple- menting such composition are choreography and orchestration.

Original: The19 choreography pattern describes decentralized collaboration directlybetween modules without a central component.
Revised: The choreography pattern describes decentralized collaboration directly between modules without a central component.

Original: The orchestration21 pattern describes collaboration through a central module, which requests the different web services and passes the intermediate results23 between them.
Revised: The orchestration pattern describes collaboration through a central module, which re- quests the different web services and passes the intermediate results between them.

Original: In the ﬁeld of image analysis, Wursch et al .
Revised: In the ﬁeld of image analysis, Wursch et al .

Original: [36] present a web- 25 based tool that enables users to perform various image analysis methods, such as text-line extraction, binarization, and layout analysis.
Revised: [38] present a webbased tool that enables users to perform various image analysis meth- ods, such as text-line extraction, binarization, and layout analysis.

Original: It is implemented using a number of Representational State Transfer (REST) web services.
Revised: It is implemented using a number of Representational State Trans- fer (REST) web services.

Original: Application examples include multiple29 web-based applications for different use cases.
Revised: Application examples include multiple web-based applications for different use cases.

Original: Further, the viability of implementing image-processing web services using REST31 has been demonstrated by Winkler et al .
Revised: Further, the viabil- ity of implementing image-processing web services using REST has been demonstrated by Winkler et al .

Original: [34], including the easeof combination of endpoints.
Revised: [36], including the ease of combination of endpoints.

Original: Another example for service-based33 image processing is Leadtools (https://www.leadtools.com), whichprovides a ﬁxed set of approx.
Revised: Another example for service-based image processing is Leadtools (https://www.leadtools.com), which provides a ﬁxed set of approx.

Original: 200 image-processing functions with35 a ﬁxed conﬁguration set via a web API.
Revised: 200 image-processing functions with a ﬁxed conﬁguration set via a web API.

Original: In this work, a similar approach using REST is chosen, however,37 with a different focus in terms of granularity of services.
Revised: In this work, a similar approach using REST is chosen, however, with a different focus in terms of granularity of services.

Original: The advantages of using microservices are ( i ) increased scalability of thecomponents, ( ii ) easy deployment and maintainability as well as,1 ( iii ) the possibility to introduce various technologies into one sys2 tem [32].
Revised: The ad- vantages of using microservices are ( i ) increased scalability of the components, ( ii ) easy deployment and maintainability as well as, ( iii ) the possibility to introduce various technologies into one system [34].

Original: For our work, we are extending a microservice platform for3 cloud-based visual analysis and processing that was ﬁrst presented4 by Richter et al .
Revised: For our work, we are extending a microservice platform for cloud-based visual analysis and processing that was ﬁrst presented by Richter et al .

Original: In addition thereto and based on that, Wegen5 et al .
Revised: In addition thereto and based on that, Wegen et al .

Original: [33] present an approach for performing service-based image6 processing using software rendering to balance cost-performance7 relation.
Revised: [35] present an approach for performing service-based image processing using software rendering to balance cost-performance relation.

Original: 8 In the ﬁeld of geodata, the Open Geospatial Consortium (OGC)9 set standards for a complete server-client ecosystem.
Revised: In the ﬁeld of geodata, the Open Geospatial Consortium (OGC) set standards for a complete server-client ecosystem.

Original: As part of this10 speciﬁcation, different web services for geodata are introduced [20].
Revised: As part of this speciﬁcation, different web services for geodata are introduced [22].

Original: 11 Each web service is deﬁned through speciﬁc input and output data12 and the ability to self-describe its functionality.
Revised: Each web service is deﬁned through speciﬁc input and output data and the ability to self-describe its functionality.

Original: In contrast, in13 the domain of general image-processing there is no such standard14 ization yet.
Revised: In contrast, in the domain of general image-processing there is no such standardization yet.

Original: However, it is possible to transfer concepts from the15 OGC standard, such as uniﬁed data models.
Revised: However, it is possible to transfer concepts from the OGC standard, such as uniﬁed data models.

Original: These data models are16 implemented using a platform-independent effect format.
Revised: These data models are implemented using a platform-independent effect format.

Original: In the17 future, it is possible to transfer even more concepts set by the OGC18 to the general image-processing domain, such as the standardized19 self-description of services.
Revised: In the future, it is possible to transfer even more concepts set by the OGC to the general image-processing domain, such as the standardized self-description of services.

Original: 3 M ETHOD
Revised: 3 M ETHOD

Original: With respect to Objective-2, we choose to implement our approach22 using microservices, which are described as follows.
Revised: With respect to Objective-2, we choose to implement our approach using microservices, which are described as follows.

Original: 3.1 System Overview
Revised: 3.1 System Overview

Original: Fig.
Revised: Fig.

Original: 3 shows a conceptual overview of the components as well as25 their data and control ﬂow.
Revised: 3 shows a conceptual overview of the components as well as their data and control ﬂow.

Original: It comprises of the following components26 that communicate via RESTful APIs:
Revised: It comprises of the following components that communicate via RESTful APIs:

Original: Moderation Browser-Extension: A client-device running a web28 browser that ( i ) hosts the moderation browser-extension and29 ( ii ) an arbitrary website with visual media content.
Revised: Moderation Browser-Extension: A client-device running a web browser that ( i ) hosts the moderation browser-extension and ( ii ) an arbitrary website with visual media content.

Original: The web30 site’s visual media content is hosted by a content provider31 and referenced by an Uniﬁed Resource Locator (URL).
Revised: The web- site’s visual media content is hosted by a content provider and referenced by an Uniﬁed Resource Locator (URL).

Original: The32 browser extension accesses the URLs via content-script and33 uses it to query the RESTful API of the CMS asynchronously.
Revised: The browser extension accesses the URLs via content-script and uses it to query the RESTful API of the CMS asynchronously.

Original: Content Moderation Service (CMS): The CMS orchestrates the35 interplay between instances of the analysis and abstraction ser36 vices, which encapsulate respective techniques.
Revised: Content Moderation Service (CMS): The CMS orchestrates the interplay between instances of the analysis and abstraction ser- vices, which encapsulate respective techniques.

Original: Upon request, it downloads the image from the given URL and forwards itscontent to an analysis service instance by querying the analysis
Revised: Upon request, it downloads the image from the given URL and forwards its content to an analysis service instance by querying the analysis

Original: RESTful API.
Revised: RESTful API.

Original: Depending on the analysis response, it uses theconﬁguration data to map analysis results to speciﬁc parametervalues that are used to query the image abstraction service.
Revised: Depending on the analysis response, it uses the conﬁguration data to map analysis results to speciﬁc parameter values that are used to query the image abstraction service.

Original: Theresponse is subsequently forwarded to the browser extensionthat replaces a placeholder content with the abstracted content.
Revised: The response is subsequently forwarded to the browser extension that replaces a placeholder content with the abstracted content.

Original: Content Analysis Service (CAS): The CAS identiﬁes if an imagecontains offensive content and has to be ﬁltered using imageabstraction techniques.
Revised: Content Analysis Service (CAS): The CAS identiﬁes if an image contains offensive content and has to be ﬁltered using image abstraction techniques.

Original: It receives image data from the CMSand performs image analysis with different image recognitionmodels as well as multiple image classiﬁcation and objectrecognition CNNs.
Revised: It receives image data from the CMS and performs image analysis with different image recognition models as well as multiple image classiﬁcation and object recognition CNNs.

Original: It then returns results of the different analysis models in a uniﬁed and structured way.
Revised: It then returns results of the different analysis models in a uniﬁed and structured way.

Original: Image Abstraction Service (IAS): The IAS provides an interfacefor applying various image abstraction techniques (e.g., blur,pixelation, or more speciﬁc operations such as cartoon stylization, etc.) with presets of different strength to images that areidentiﬁed by the CAS to possibly contain offensive content.
Revised: Image Abstraction Service (IAS): The IAS provides an interface for applying various image abstraction techniques (e.g., blur, pixelation, or more speciﬁc operations such as cartoon styliza- tion, etc.) with presets of different strength to images that are identiﬁed by the CAS to possibly contain offensive content.

Original: 3.2 Browser-Extension for Moderation Client
Revised: 3.2 Browser-Extension for Moderation Client

Original: [Figure] The browser extension tra- 21 verses the Document Object Model (DOM) tree and utilizes23 a MutationObserver object todetect changes in the respec- 25 tive image and picture tags;a DOM-MutationObserver is27 provided by the correspondingweb browser and is intended to29 watch for changes being madeto the DOM tree.
Revised: The browser extension traverses the Document Object Model (DOM) tree and utilizes a MutationObserver object to detect changes in the respective image and picture tags; a DOM-MutationObserver is provided by the corresponding web browser and is intended to watch for changes being made to the DOM tree.

Original: As soon as31 an image is detected, it is initially blurred using Cascading33 Style Sheets (CSS) image ﬁlters.
Revised: As soon as an image is detected, it is initially blurred using Cascading Style Sheets (CSS) image ﬁlters.

Original: This prevents users to see possibledisturbing image content while the CMS’s RESTful API is queried35 and the image’s URL is transmitted to it.
Revised: This prevents users to see possible disturbing image content while the CMS’s RESTful API is queried and the image’s URL is transmitted to it.

Original: The response received bythe CMS contains information on whether or not an image contains37 disturbing content and therefore needs to be disguised.
Revised: The response received by the CMS contains information on whether or not an image contains disturbing content and therefore needs to be disguised.

Original: In the casethat an image is categorized as offensive, the response also includes39 a processed version of the image.
Revised: In the case that an image is categorized as offensive, the response also includes a processed version of the image.

Original: Subsequent to receiving the response, the local CSS-ﬁlter blur is41 removed.
Revised: Subsequent to receiving the response, the local CSS-ﬁlter blur is removed.

Original: If the processed image does contain suggestive content,the original image gets replaced – otherwise, the original image is43 displayed.
Revised: If the pro- cessed image does contain suggestive content, the original im- age gets replaced – otherwise, the original image is displayed.

Original: Finally, an overlay is added for every image (Fig. 4) thatprovides buttons for ( i ) allowing users to report misclassiﬁed images 45 and ( ii ) toggling between the original and the processed image.
Revised: Finally, an overlay is added for every image (Fig. 4) that pro- vides buttons for ( i ) allowing users to report misclassiﬁed im- ages and ( ii ) toggling between the original and the processed image.

Original: If auser does not agree with the determined content classiﬁcation, they47 can propose a more suitable one.
Revised: If a user does not agree with the determined content classiﬁcation, they can propose a more suitable one.

Original: A modal (Fig. 5) will appear wherethe user can select if another category is more suitable, the image is49 disturbing in a different way, or it should not be ﬁltered.
Revised: A modal (Fig. 5) will appear where the user can select if another category is more suitable, the image is disturbing in a different way, or it should not be ﬁltered.

Original: 3.3 Content Moderation Service
Revised: 3.3 Content Moderation Service

Original: The CMS moderates communication and interactions between the52 browser extension, the analysis service, and the image abstractionservice.
Revised: The CMS moderates communication and interactions between the browser extension, the analysis service, and the image abstraction service.

Original: Clients use it to initiate the analysis process that consists of54 the following steps.
Revised: Clients use it to initiate the analysis process that consists of the following steps.

Original: First, the image is downloaded using the URLspeciﬁed in the analysis request.
Revised: First, the image is downloaded using the URL speciﬁed in the analysis request.

Original: Then, the image analyzer is queried56 to detect if the image contains offensive content.
Revised: Then, the image analyzer is queried to detect if the image contains offensive content.

Original: Subsequently,the CMS maps the image analysis results to an image abstraction58 technique and forwards it to the abstraction service for application.
Revised: Subsequently, the CMS maps the image analysis results to an image abstraction technique and forwards it to the abstraction service for application.

Original: Finally, it notiﬁes the client whether the image contains offensive60 content and, if it does, attaches the processed image to the response.
Revised: Finally, it notiﬁes the client whether the image contains offensive content and, if it does, attaches the processed image to the response.

Original: If a user decides to send feedback via the feedback modal (e.g., the62 chosen scenario is unsuitable), a request to a feedback route is sentand the feedback is stored for further use.
Revised: If a user decides to send feedback via the feedback modal (e.g., the chosen scenario is unsuitable), a request to a feedback route is sent and the feedback is stored for further use.

Original: 3.4 Content Analysis & Abstraction Service
Revised: 3.4 Content Analysis & Abstraction Service

Original: The CAS provides an interface for clients to analyze images for the2 presence of certain objects and categories.
Revised: The CAS provides an interface for clients to analyze images for the presence of certain objects and categories.

Original: Clients send the image3 data and receive analysis results that are represented in the form4 of tags with score and meta data associated.
Revised: Clients send the image data and receive analysis results that are represented in the form of tags with score and meta data associated.

Original: Tags can comprise5 objects displayed in a image or categories that can be associated6 with an image.
Revised: Tags can comprise objects displayed in a image or categories that can be associated with an image.

Original: The score describes how likely an object or category7 is present.
Revised: The score describes how likely an object or category is present.

Original: The meta data could include an Axis-aligned Bounding8 Box (AABB) that describes the estimated position of an object9 within the image.
Revised: The meta data could include an Axis-aligned Bounding Box (AABB) that describes the estimated position of an object within the image.

Original: 10 The image analysis is performed with different machine learning11 models, in our case using CNNs.
Revised: The image analysis is performed with different machine learning models, in our case using CNNs.

Original: The output, speciﬁc to each model12 used, must be transformed to the uniﬁed analysis result format.
Revised: The output, speciﬁc to each model used, must be transformed to the uniﬁed analysis result format.

Original: This allows extending the analysis service with additional Machine14 Learning (ML) models.
Revised: This allows extending the analysis service with additional Machine Learning (ML) models.

Original: The results of the image analysis are used 15 by the content moderator to decide what kind of content an image is16 of and whether an image abstraction is applied.
Revised: The results of the image analysis are used [Figure] by the content moderator to decide what kind of content an image is of and whether an image abstraction is applied.

Original: 17 If offensive content was detected on an image, it is disguised by18 applying an image abstraction techniques to it.
Revised: If offensive content was detected on an image, it is disguised by applying an image abstraction techniques to it.

Original: The IAS provides an19 endpoint to apply speciﬁc techniques such as pixelation, or blur to20 an image.
Revised: The IAS provides an endpoint to apply speciﬁc techniques such as pixelation, or blur to an image.

Original: For every technique, different presets and parameters are21 provided to indicate different degrees and styles of image abstraction.
Revised: For every technique, different presets and parameters are provided to indicate different degrees and styles of image abstraction.

Original: 22 To query the abstraction endpoint, the IAS requires the image’s data23 as well as a mapped abstraction techniques and its preset (Sec. 4).
Revised: To query the abstraction endpoint, the IAS requires the image’s data as well as a mapped abstraction techniques and its preset (Sec. 4).

Original: 24 With respect to this, the CMS perform such mapping by taking the25 analyzed scenario and score into account.
Revised: With respect to this, the CMS perform such mapping by taking the analyzed scenario and score into account.

Original: In response, the processed26 image is returned by the IAS.
Revised: In response, the processed image is returned by the IAS.

Original: 4 M APPING OF A NALYSIS R ESULTS
Revised: 4 M APPING OF A NALYSIS R ESULTS

Original: The analysis result for an image is a set of tags with scores.
Revised: The analysis result for an image is a set of tags with scores.

Original: The29 tags describe objects that can be displayed or categories that can be30 associated with an image.
Revised: The tags describe objects that can be displayed or categories that can be associated with an image.

Original: The scores describe how likely these tags31 are actually present on an image.
Revised: The scores describe how likely these tags are actually present on an image.

Original: An image abstraction in the sense32 of content moderation, is processing a user generated content image33 with an image abstraction technique with a speciﬁc parameter preset.
Revised: An image abstraction in the sense of content moderation, is processing a user generated content image with an image abstraction technique with a speciﬁc parameter preset.

Original: 34 To process images with the goal of reducing explicit content, one35 has to deﬁne a mapping from analysis results to an image abstraction36 technique with a speciﬁc parameter preset.
Revised: To process images with the goal of reducing explicit content, one has to deﬁne a mapping from analysis results to an image abstraction technique with a speciﬁc parameter preset.

Original: In the proposed system each tag was manually associated with a38 scenario.
Revised: In the proposed system each tag was manually associated with a scenario.

Original: A scenario is a type of content that should be moderated.
Revised: A scenario is a type of content that should be moderated.

Original: 39 For this system, the scenarios nudity , violence , and medical are40 used.
Revised: For this system, the scenarios nudity , violence , and medical are used.

Original: Each scenario is speciﬁed by: ( i ) name, ( ii ) set of tag names41 and threshold, ( iii ) image abstraction technique, and ( iv ) three ef- fect presets, sorted by degree of abstraction (low, medium, strong)
Revised: Each scenario is speciﬁed by: ( i ) name, ( ii ) set of tag names and threshold, ( iii ) image abstraction technique, and ( iv ) three effect presets, sorted by degree of abstraction (low, medium, strong).

Original: 43 A scenario is matched to analysis results if any of the scenario’s44 tag names are contained in the received analysis’ tags and the re45 ceived score is equal or higher than the scenario tag score threshold.
Revised: A scenario is matched to analysis results if any of the scenario’s tag names are contained in the received analysis’ tags and the re- ceived score is equal or higher than the scenario tag score threshold.

Original: 46 Because an analysis result could match multiple scenarios, the sce47 narios are prioritized and the matching scenario with the highest[Figure] priority is chosen.
Revised: Because an analysis result could match multiple scenarios, the sce- narios are prioritized and the matching scenario with the highest priority is chosen.

Original: If no scenario matches, then no image abstraction1 is required.
Revised: If no scenario matches, then no image abstraction is required.

Original: Otherwise, the user-deﬁned preset will be selected andused for the subsequent image abstraction step.
Revised: Otherwise, the user-deﬁned preset will be selected and used for the subsequent image abstraction step.

Original: Fig.
Revised: Fig.

Original: 6 shows four images with similar content but different map-4 pings.
Revised: 6 shows four images with similar content but different map- pings.

Original: All four images depict scenes with weapons and are categorized as showing violent content by the CAS.
Revised: All four images depict scenes with weapons and are categorized as showing violent content by the CAS.

Original: The used mappings6 are chosen according to the score that indicates how likely theseimages show violent content.
Revised: The used mappings are chosen according to the score that indicates how likely these images show violent content.

Original: Fig.
Revised: Fig.

Original: 6(a) is disguised using a Gaussian8 blur preset with a large kernel size (Fig. 6(b)).
Revised: 6(a) is disguised using a Gaussian blur preset with a large kernel size (Fig. 6(b)).

Original: Fig.
Revised: Fig.

Original: 6(d) shows anapplied cartoon ﬁlter using a black & white preset with thin edges10 to remove color information.
Revised: 6(d) shows an applied cartoon ﬁlter using a black & white preset with thin edges to remove color information.

Original: Fig.
Revised: Fig.

Original: 6(h) uses an cartoon ﬁlter withthick edges.
Revised: 6(h) uses an cartoon ﬁlter with thick edges.

Original: Fig. 6(h) show the results of a pixelation abstraction12 technique for aggressive disguise.
Revised: Fig. 6(h) show the results of a pixelation abstraction technique for aggressive disguise.

Original: The effect that is mapped to animage is arbitrary and can be customized, but different abstraction14 techniques are more or less suitable for certain scenarios.
Revised: The effect that is mapped to an image is arbitrary and can be customized, but different abstraction techniques are more or less suitable for certain scenarios.

Original: In particular, this work uses a pixelation technique for images showing violent16 content, a cartoon ﬁlter for medical content based on the work of
Revised: In particular, this work uses a pixelation technique for images showing violent content, a cartoon ﬁlter for medical content based on the work of

Original: Winnem¨oller et al .
Revised: Winnem¨oller et al .

Original: [35] and as suggested by the studies of Besanc¸on18 et al .
Revised: [37] and as suggested by the studies of Besanc¸on et al .

Original: [1,2], and a Gaussian blur for images that depict nudity.
Revised: [2,3], and a Gaussian blur for images that depict nudity.

Original: 5 I MPLEMENTATION A SPECTS
Revised: 5 I MPLEMENTATION A SPECTS

Original: In a service-based architecture similar to the one used in the content21 moderation scenario, it is necessary that messages are exchangedbetween the individual services.
Revised: In a service-based architecture similar to the one used in the content moderation scenario, it is necessary that messages are exchanged between the individual services.

Original: Therefore, each service provides23 a RESTful API and queries other services correspondingly.
Revised: Therefore, each service provides a RESTful API and queries other services correspondingly.

Original: Thebrowser extension is implemented using JavaScript (JS) and utilizes1 the used browser’s API to access and alter a website’s DOM tree to2 administer local storage and to react on changes made to the website.
Revised: The browser extension is implemented using JavaScript (JS) and utilizes the used browser’s API to access and alter a website’s DOM tree to administer local storage and to react on changes made to the website.

Original: 3 For sending requests to other services, the fetch API is applied.
Revised: For sending requests to other services, the fetch API is applied.

Original: Filter4 options facilitate users to customize whether and to what extend5 suggestive images should be abstracted and all three scenarios can6 be customized individually.
Revised: Filter options facilitate users to customize whether and to what extend suggestive images should be abstracted and all three scenarios can be customized individually.

Original: Users can choose among three levels of7 abstraction or can switch off single scenarios completely.
Revised: Users can choose among three levels of abstraction or can switch off single scenarios completely.

Original: 8 The CMS is implemented using Node.js and provides a RESTful9 API with two endpoints: one for requesting an image to be analyzed10 and categorized and one for sending user feedback.
Revised: The CMS is implemented using Node.js and provides a RESTful API with two endpoints: one for requesting an image to be analyzed and categorized and one for sending user feedback.

Original: A request sent11 to the analyze endpoint needs to include the URL of the image that12 should be analyzed and options that represent the ﬁlter settings made13 by the user.
Revised: A request sent to the analyze endpoint needs to include the URL of the image that should be analyzed and options that represent the ﬁlter settings made by the user.

Original: A feedback request consists of three different pieces of14 information: the data concerning the assessed image, the category15 proposed by the image analyzer service, and the category included16 in the user’s feedback.
Revised: A feedback request consists of three different pieces of information: the data concerning the assessed image, the category proposed by the image analyzer service, and the category included in the user’s feedback.

Original: This data is stored and used as a training17 set for machine learning algorithms that can be used during image18 classiﬁcation.
Revised: This data is stored and used as a training set for machine learning algorithms that can be used during image classiﬁcation.

Original: The implementation of the IAS also relies on Node.js.
Revised: The implementation of the IAS also relies on Node.js.

Original: 19 It provides an endpoint that accepts requests that need to include20 the data of the image to be abstracted and an operation that should21 be applied to the image.
Revised: It provides an endpoint that accepts requests that need to include the data of the image to be abstracted and an operation that should be applied to the image.

Original: A preset, related to the desired effect, can22 also be sent to this endpoint as an optional parameter.
Revised: A preset, related to the desired effect, can also be sent to this endpoint as an optional parameter.

Original: The CAS is23 implemented using Python and ﬂask.
Revised: The CAS is implemented using Python and ﬂask.

Original: It provides a RESTful API24 with an endpoint to start the analysis process.
Revised: It provides a RESTful API with an endpoint to start the analysis process.

Original: 25 For the basic functionality of the CMS, two different kinds of26 neural networks are used: ( i ) a Single Shot MultiBox model [17] and27 ( ii ) the Yahoo Open NSFW model [19].
Revised: For the basic functionality of the CMS, two different kinds of neural networks are used: ( i ) a Single Shot MultiBox model [19] and ( ii ) the Yahoo Open NSFW model [21].

Original: Single Shot MultiBox is a28 CNN architecture that performs object recognition on images.
Revised: Single Shot MultiBox is a CNN architecture that performs object recognition on images.

Original: For a29 given image it returns AABB with a scenario and a conﬁdence  score30 (0 to 1).
Revised: For a given image it returns AABB with a scenario and a conﬁdence [Figure] score (0 to 1).

Original: The conﬁdence score indicates to what extend a scenario is31 detected in the image.
Revised: The conﬁdence score indicates to what extend a scenario is detected in the image.

Original: An existing implementation for PyTorch was32 used, as well as a classiﬁcation model that has been initially trained33 on the Pascal VOC dataset [4], but with very general object classes34 such as aeroplane, car, cow, dog, or TV monitor.
Revised: An existing implementation for PyTorch was used, as well as a classiﬁcation model that has been initially trained on the Pascal VOC dataset [6], but with very general object classes such as aeroplane, car, cow, dog, or TV monitor.

Original: To this end, we also35 trained an SSD model on military-like classes of the Google Open36 Images Dataset [14] (such as riﬂe, tank, knife, missile) to be able to37 make predictions on somewhat realistic explicit content.
Revised: To this end, we also trained an SSD model on military-like classes of the Google Open Images Dataset [16] (such as riﬂe, tank, knife, missile) to be able to make predictions on somewhat realistic explicit content.

Original: The Yahoo38 Open NSFW model also returns for a given image an NSFW score39 (0 – safe, no nudity detected; to 1 – not safe, nudity detected) and an40 implementation and trained model for TensorFlow is available, but a41 proper threshold for this score is required.
Revised: The Yahoo Open NSFW model also returns for a given image an NSFW score (0 – safe, no nudity detected; to 1 – not safe, nudity detected) and an implementation and trained model for TensorFlow is available, but a proper threshold for this score is required.

Original: Such threshold might be42 different according to the use case of the system and must be chosen43 carefully.
Revised: Such threshold might be different according to the use case of the system and must be chosen carefully.

Original: 6 R ESULTS AND D ISCUSSION 
Revised: 6 R ESULTS AND D ISCUSSION 6.1 Performance Evaluation

Original: 6.1 Applications

Original: The system described in this work offers advantages for the con47 sumption, use, and eventually moderation of graphic content in 48 different areas we now detail.

Original: First of all, it could assist for medical 49 and surgical education.

Original: A primary use case would be to facilitate 50 the education of nurses and medical students (not all destined to 51 be surgeons) by reducing affection and aversion when looking at 52 images showing blood and medical acts [1,2].

Original: Similarly, another use 53 case is on the communication between surgeons and patients [1,2].

Original: 54 Patients are usually informed and prepared before the planning of 55 future surgeries and the explanations can be facilitated by the use 56 of images.

Original: Yet, laypeople often ﬁnd looking at images depicting 57 surgery or blood extremely difﬁcult [6, 27].

Original: Communication be58 tween patients and doctors could therefore be improved with such 59 automated image processing tools.

Original: 60 Furthermore, the system can be used to moderate internet forums 61 and social networks.

Original: Nowadays, a lot of digital media, including 62 images and videos, are shared through social media platforms such 63 as Facebook or Instagram.

Original: While graphic content sometimes adheres 64 to the Terms of Services (TOSs) of these platforms [5,30], many

Original: [Figure]

Original: graphic media are not accepted for ethical or legal reasons.

Original: The ﬁl- 1 tering between authorized and non-authorized content is performedby a combination of algorithms and people, or people alone (content 3 moderators) depending on the platform.

Original: The software system presented in this paper could facilitate a content moderator’s work and 5 help to prevent mental issues that can be a consequence of lookingat disturbing pictures all day [21].

Original: The system could also alleviate 7 the toll on volunteer moderators of platforms such as Wikipediaor Reddit.

Original: In a similar fashion, journalists and news editors might 9 have to browse through hundreds of shocking content to illustratetheir articles or better understand the case they are reporting on (e.g., 11 war-zones, disasters, accidents).

Original: Our automated tool could also beuseful in this speciﬁc context.

Original: Finally, since exposure to graphic or pornographic content has 14 been shown to be particularly detrimental to children [16], our toolcould particularly be interesting on this regard.

Original: While blocking 16 software could be used to limit access to nudity or pornography, suchsoftware tend to also limit access to useful information (e.g., online 18 sex education) [24], are rarely maintained or even used [3], andunlikely to block access to content on some social media platforms.

Original: 20 Blocking software ﬁnally rarely target all sorts of graphic content.

Original: With respect to this issue, we hope that our tool can help limit 22 the impact of unwanted graphic content, rather than eliminate itcompletely along with potentially useful information.

Original: 6.2 Performance Evaluation

Original: We only focus here on system performance as the potential reduc2 tion of affective responses through image abstraction techniques3 has already been studied [1,2,37].
Revised: We only focus here on system performance as the potential reduc- tion of affective responses through image abstraction techniques has already been studied [2,3,39].

Original: The system’s performance was4 evaluated by timing different tasks involved in the process of content5 moderation and comparing these to assemble a metric of the requests6 and processed images (time stamp, image resolution, image transfor7 mation).
Revised: The system’s performance was evaluated by timing different tasks involved in the process of content moderation and comparing these to assemble a metric of the requests and processed images (time stamp, image resolution, image transfor- mation).

Original: Timed tasks measured on the CMS were ( i ) fetching image,8 ( ii ) performing image analysis, and ( iii ) performing image transfor9 mation by abstraction.
Revised: Timed tasks measured on the CMS were ( i ) fetching image, ( ii ) performing image analysis, and ( iii ) performing image transfor- mation by abstraction.

Original: The CMS, CAS, and IAS are hosted on a10 single dedicated GPU-server without a signiﬁcant network overhead.
Revised: The CMS, CAS, and IAS are hosted on a single dedicated GPU-server without a signiﬁcant network overhead.

Original: Thus, the network request times between clients and the services are12 assumed to be independent of our system and are not considered.
Revised: Thus, the network request times between clients and the services are assumed to be independent of our system and are not considered.

Original: To13 evaluate the performance in a way that considers all required tasks14 equally, only requests that led to an image transformation (unsafe15 image was detected) were considered.
Revised: To evaluate the performance in a way that considers all required tasks equally, only requests that led to an image transformation (unsafe image was detected) were considered.

Original: The dedicated GPU-server16 is equipped with Xeon E5-2637 v4, 3 .
Revised: The dedicated GPU-server is equipped with Xeon E5-2637 v4, 3 .

Original: 5GHz processor (8 cores),17 64GB RAM, NVIDIA Quadro M

Revised: 5GHz processor (8 cores), 64GB RAM, NVIDIA Quadro M

Original: GPU with 24GB VRAM.
Revised: GPU with 24GB VRAM.

Original: Over a week of testing the extension, about 35000 requests in19 volving image transformation are logged in total.
Revised: Over a week of testing the extension, about 35000 requests in- volving image transformation are logged in total.

Original: Fig. 7(a) and20 Fig.
Revised: Fig. 7(a) and

Original:  7(b) show the distribution of total request times per days and21 hours.
Revised: Fig. 7(b) show the distribution of total request times per days and hours.

Original: These are mostly independent of each day and hour with22 slight variances, which could be explained by a varying load of the23 server or different number of requests incoming in a shorter period24 of time.
Revised: These are mostly independent of each day and hour with slight variances, which could be explained by a varying load of the server or different number of requests incoming in a shorter period of time.

Original: Fig.
Revised: Fig.

Original: 8 shows the mean time required for each task per day.
Revised: 8 shows the mean time required for each task per day.

Original: 25 The image analysis requires ≈ 75% of the mean request time, with26 some outliers on day 6 where fetching the images suddenly takes up27 as much time as analyzing the images on average.
Revised: The image analysis requires ≈ 75% of the mean request time, with some outliers on day 6 where fetching the images suddenly takes up as much time as analyzing the images on average.

Original: Fig.
Revised: Fig.

Original: 9(a) shows28 the resolution of images over the time required for analysis.
Revised: 9(a) shows the resolution of images over the time required for analysis.

Original: We further tested whether images of high resolution impact anal30 ysis performance.
Revised: We further tested whether images of high resolution impact anal- ysis performance.

Original: The documentation of the used CNNs describe31 that image data is propagated through the networks at a constant res32 olution, i.e., a downsampling is required before propagation through33 the CNNs.
Revised: The documentation of the used CNNs describe that image data is propagated through the networks at a constant res- olution, i.e., a downsampling is required before propagation through the CNNs.

Original: Fig.
Revised: Fig.

Original: 9(a) shows that images have similar analysis times34 independently of the resolution and the required downsampling step.
Revised: 9(a) shows that images have similar analysis times independently of the resolution and the required downsampling step.

Original: 35 A further question relates whether very small images (such as icons)36 cause a performance overhead if they appear on websites very often.
Revised: A further question relates whether very small images (such as icons) cause a performance overhead if they appear on websites very often.

Original: 37 Images that are smaller than 32 × 32 pixels are highlighted in red38 within Fig. 9(a).
Revised: Images that are smaller than 32 × 32 pixels are highlighted in red within Fig. 9(a).

Original: One can see that they need similar inference times39 compared to all other images.
Revised: One can see that they need similar inference times compared to all other images.

Original: Further analysis of the statistics also40 indicate that they take up only ≈ 5% in count of all images and41 less than one percent in total request time.
Revised: Further analysis of the statistics also indicate that they take up only ≈ 5% in count of all images and less than one percent in total request time.

Original: A similar analysis was42 performed for the image transformation task.
Revised: A similar analysis was performed for the image transformation task.

Original: Relating to the image[Figure] resolution vs.the image transformation (Fig. 9(b)) shows a linear1 dependency between image resolution (as number of total pixels)and transformation time.
Revised: Relating to the image resolution vs.the image transformation (Fig. 9(b)) shows a linear dependency between image resolution (as number of total pixels) and transformation time.

Original: However, this does not impact the com-3 plete performance severely as image analysis is slower by a factorof about 10.
Revised: However, this does not impact the com- plete performance severely as image analysis is slower by a factor of about 10.

Original: Small images have been highlighted, again they take5 up ≈ 5% in count and about ≈ 4% in total time only.
Revised: Small images have been highlighted, again they take up ≈ 5% in count and about ≈ 4% in total time only.

Original:  Finally, we evaluated whether a high load (possibly caused by a7 high number of requests in a short time) causes higher total requesttimes.
Revised: [Figure] Finally, we evaluated whether a high load (possibly caused by a high number of requests in a short time) causes higher total request times.

Original: For it, each request is plotted as a point with the number of9 requests in that minute and its required time (Fig. 10).
Revised: For it, each request is plotted as a point with the number of requests in that minute and its required time (Fig. 10).

Original: This doesnot show any strong correlation between the number of requests per11 minute and the total request time though; perhaps, the load generatedby the requests was not high enough yet.
Revised: This does not show any strong correlation between the number of requests per minute and the total request time though; perhaps, the load generated by the requests was not high enough yet.

Original: 6.3 Limitations
Revised: 6.2 Applications

Revised: The system described in this work offers advantages for the con- sumption, use, and eventually moderation of graphic content in different areas we now detail.

Revised: First of all, it could assist for medical and surgical education.

Revised: A primary use case would be to facilitate the education of nurses and medical students (not all destined to be surgeons) by reducing affection and aversion when looking at images showing blood and medical acts [2,3].

Revised: Similarly, another use case is on the communication between surgeons and patients [2,3].

Revised: Patients are usually informed and prepared before the planning of future surgeries and the explanations can be facilitated by the use of images.

Revised: Yet, laypeople often ﬁnd looking at images depicting surgery or blood extremely difﬁcult [8, 29].

Revised: Communication between patients and doctors could therefore be improved with such automated image processing tools.

Revised: Furthermore, the system can be used to moderate internet forums and social networks.

Revised: Nowadays, a lot of digital media, including images and videos, are shared through social media platforms such as Facebook or Instagram.

Revised: While graphic content sometimes adheres to the Terms of Services (TOSs) of these platforms [7,32], many graphic media are not accepted for ethical or legal reasons.

Revised: The ﬁltering between authorized and non-authorized content is performed by a combination of algorithms and people, or people alone (content moderators) depending on the platform.

Revised: The software system pre- sented in this paper could facilitate a content moderator’s work and help to prevent mental issues that can be a consequence of looking at disturbing pictures all day [23].

Revised: The system could also alleviate the toll on volunteer moderators of platforms such as Wikipedia or Red- dit [4,15].

Revised: In a similar fashion, journalists and news editors might have to browse through hundreds of shocking content to illustrate their articles or better understand the case they are reporting on (e.g., war-zones, disasters, accidents).

Revised: Our automated tool could also be useful in this speciﬁc context.

Revised: Finally, since exposure to graphic or pornographic content has been shown to be particularly detrimental to children [18], our tool could particularly be interesting on this regard.

Revised: While blocking software could be used to limit access to nudity or pornography, such software tend to also limit access to useful information (e.g., online sex education) [26], are rarely maintained or even used [5], and unlikely to block access to content on some social media platforms.

Revised: Blocking software ﬁnally rarely target all sorts of graphic content.

Revised: With respect to this issue, we hope that our tool can help limit the impact of unwanted graphic content, rather than eliminate it completely along with potentially useful information.

Revised: 6.3 Limitations

Original: Applying speciﬁc ﬁlters to weaken affect when looking at medical15 images might work differently or not at all depending on the user andthe speciﬁc image at hand.
Revised: Applying speciﬁc ﬁlters to weaken affect when looking at medical images might work differently or not at all depending on the user and the speciﬁc image at hand.

Original: More generally, it is impossible to ﬁnd a17 deﬁnition of offensive content that ﬁts all users.
Revised: More generally, it is impossible to ﬁnd a deﬁnition of offensive content that ﬁts all users.

Original: What is perceivedas “graphic” highly depends on the perception, age, views, cultural19 background, and personal history of the user, which results in manydifferent potential use cases for each individual user.
Revised: What is perceived as “graphic” highly depends on the perception, age, views, cultural background, and personal history of the user, which results in many different potential use cases for each individual user.

Original: Even if a clear21 deﬁnition would be possible, modern computer vision approachesare not able to correctly recognize offensive content all of the time.
Revised: Even if a clear deﬁnition would be possible, modern computer vision approaches are not able to correctly recognize offensive content all of the time.

Original: Additionally, detecting objects that are known to be offensive is not1 enough.
Revised: Additionally, detecting objects that are known to be offensive is not enough.

Original: The context in which these objects are as well as the overall2 image composition could completely change the meaning.
Revised: The context in which these objects are as well as the overall image composition could completely change the meaning.

Original: 3 We do nothave the capacities to train accurate neural networks4 for real use-cases because there are no acceptable, publicly available5 datasets of offensive content.
Revised: We do not train accurate neural networks for real use-cases because there are no acceptable, publicly available datasets of offensive content.

Original: Even with a proper dataset, it is6 unlikely to train a perfect neural network, which classiﬁes all images7 correctly and detects offensive content every time.
Revised: Even with a proper dataset, it is unlikely to train a perfect neural network, which classiﬁes all images correctly and detects offensive content every time.

Original: 8 Regarding the browser extension, it is difﬁcult to even detect all9 the images on websites since there are a number of different ways10 to integrate an image into a web page, e.g., through custom HTML11 elements or extensive JS usage.
Revised: Regarding the browser extension, it is difﬁcult to even detect all the images on websites since there are a number of different ways to integrate an image into a web page, e.g., through custom HTML elements or extensive JS usage.

Original: If images are loaded asynchronously12 via JS and many images change simultaneously, the extension is13 not able to react fast enough to all of the changes, resulting in “un14 obfuscated” or “non-abstracted” images.
Revised: If images are loaded asynchronously via JS and many images change simultaneously, the extension is not able to react fast enough to all of the changes, resulting in “unobfuscated” or “non-abstracted” images.

Original: Moreover, JS is executed15 in a single thread in all established web browsers, which increased16 the occurrence of time-related problems.
Revised: Moreover, JS is executed in a single thread in all established web browsers, which increased the occurrence of time-related problems.

Original: Additionally, a very low17 bandwidth could be slowing down the processing of images.
Revised: Additionally, a very low bandwidth could be slowing down the processing of images.

Original: The18 impact would be comparably tiny because not much additional data19 is sent and the client still downloads each image only once.
Revised: The impact would be comparably tiny because not much additional data is sent and the client still downloads each image only once.

Original: 7 C ONCLUSIONS AND F UTURE W ORK
Revised: 7 C ONCLUSIONS AND F UTURE W ORK

Original: This paper presents a service-based approach to facilitate consump22 tion of digital graphic images online.
Revised: This paper presents a service-based approach to facilitate consump- tion of digital graphic images online.

Original: To achieve this, an automatic23 analysis and classiﬁcation of possibly offensive content is performed24 using services, and, based on the results, image abstraction tech25 niques are applied with varying levels of abstraction.
Revised: To achieve this, an automatic analysis and classiﬁcation of possibly offensive content is performed using services, and, based on the results, image abstraction techniques are applied with varying levels of abstraction.

Original: This func26 tionality is accessed and conﬁgured via a browser extension that27 is supported by most modern web browsers.
Revised: This func- tionality is accessed and conﬁgured via a browser extension that is supported by most modern web browsers.

Original: The presented con28 tent moderation approach has various applications such as reducing29 affective responses during medical education, allowing less distress30 ing browsing of social media, or enabling safer browsing for child31 protection.
Revised: The presented con- tent moderation approach has various applications such as reducing affective responses during medical education, allowing less distress- ing browsing of social media, or enabling safer browsing for child protection.

Original: 32 Regarding future work, the user experience of the extension could33 be increased as follows.
Revised: Regarding future work, the user experience of the extension could be increased as follows.

Original: Modern object recognition algorithms are34 not only able to detect certain objects in images but are also able to35 locate them.
Revised: Modern object recognition algorithms are not only able to detect certain objects in images but are also able to locate them.

Original: With respect to this, image abstraction techniques can36 be applied to segments of the image to maintain the context and only37 abstract the sensitive image regions.
Revised: With respect to this, image abstraction techniques can be applied to segments of the image to maintain the context and only abstract the sensitive image regions.

Original: This might support the user to38 identify whether an image was classiﬁed correctly.
Revised: This might support the user to identify whether an image was classiﬁed correctly.

Original: As an alternative,39 the image abstraction techniques could be applied to the complete40 image, but the user could be given the option to interactively reveal 
Revised: As an alternative, the image abstraction techniques could be applied to the complete image, but the user could be given the option to interactively reveal the image partial using lens-based interaction metaphors. A CKNOWLEDGMENTS

Revised: This work was funded by the German Federal Ministry of Education and Research (BMBF) through grant 01IS18092 (”mdViPro”) and 01IS19006 (”KI-LAB-ITSE”)