Reverse Image Search Research Articles

Media Literacy and Fact-Checking as Proactive and Reactive Responses to Misinformation in Kenya and Senegal

ABSTRACT This study examines how media professionals in Kenya and Senegal respond to misinformation using fact-checking and media literacy strategies and how they perceive these strategies for mitigating the spread of misinformation. We conducted 42 semi-structured in-depth interviews with people belonging to one of the following three groups: individuals employed in the news media such as journalists, reporters and editors; individuals working for either independent fact-checking organisations or doing fact-checking within the newsrooms of online and offline media; and individuals engaged in media policymaking. Findings indicate that media professionals in both Senegal and Kenya employ fact-checking strategies such as cross-checking information from primary, secondary and expert sources and verifying images and videos using reverse image search as reactive strategies. Consistent with inoculation theory, participants also promote media literacy as a proactive strategy to equip media producers and consumers with the knowledge and skills to critically engage with media content. Lastly, journalists, fact-checkers and policymakers lauded fact-checking and media literacy as effective strategies for mitigating the spread of misinformation, suggesting that both strategies should be used simultaneously to empower both media consumers and producers against misinformation.

Cross-modality sub-image retrieval using contrastive multimodal image representations

In tissue characterization and cancer diagnostics, multimodal imaging has emerged as a powerful technique. Thanks to computational advances, large datasets can be exploited to discover patterns in pathologies and improve diagnosis. However, this requires efficient and scalable image retrieval methods. Cross-modality image retrieval is particularly challenging, since images of similar (or even the same) content captured by different modalities might share few common structures. We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across modalities, which combines deep learning to generate representations (embedding the different modalities in a common space) with robust feature extraction and bag-of-words models for efficient and reliable retrieval. We illustrate its advantages through a replacement study, exploring a number of feature extractors and learned representations, as well as through comparison to recent (cross-modality) CBIR methods. For the task of (sub-)image retrieval on a (publicly available) dataset of brightfield and second harmonic generation microscopy images, the results show that our approach is superior to all tested alternatives. We discuss the shortcomings of the compared methods and observe the importance of equivariance and invariance properties of the learned representations and feature extractors in the CBIR pipeline. Code is available at: https://github.com/MIDA-group/CrossModal_ImgRetrieval.

Can't read my broker face?—Tracing a motif and metaphor of expert knowledge through audiovisual images of the financial crisis

AbstractBased on the question of the representability of economy and economics in audiovisual media, developments on the financial markets have often been discussed as a depiction problem. The abstractness and complexity of economic interrelations seem to defy classical modes of storytelling and dramatization. Nevertheless, public opinion about economic changes and dependencies crucially relies on audiovisual media. But how can the public communicate in images, sounds, and words about forces that are out of sight and out of reach, and can supposedly only be adequately grasped by experts? In a case study on audiovisual images of the global financial crisis (2007–), this paper tracks and analyzes a recurring motif: the staging of expert knowledge as close‐ups of expressive faces vis‐à‐vis computer screens in television news, documentaries, as well as feature films. It draws on the use of digital tools for corpus exploration (reverse image search) and the visualization of video annotations. By relating and comparing different staging strategies by which these “broker faces” become embodiments of turbulent market dynamics, the paper proposes to not regard them as repeated instantiations of the same metaphor, but as a developing web of cinematic metaphors. Different perspectives (news of market developments or historical accounts of crisis developments) and affective stances toward the global financial crisis are expressed in these variations of the face‐screen constellation. The paper thus presents a selection of different appearances of “broker faces” as a medium for an audiovisual discourse of the global financial crisis. A concluding analysis of a scene from Margin Call focuses on its specific intertwining of expert and screen as an ambivalent movement figuration of staging insight. Between the feeling of discovery (of a potential future threat) and the sense of being haunted (by a menacing force), the film stages the emergence of a “broker face” in an atmospheric tension between suspense and melancholy. We argue that the film thereby reframes the motif and poses questions of agency, temporality, and expert knowledge.

The effectiveness of a virtual learning companion for supporting the critical judgment of social media content

Social media usage has become a daily habit for the younger generation. It can have positive effects on educational processes, but it also raises concerns about harmful content, such as fake news or hate speech. Fake news is often distributed with the intention to manipulate the public opinion by propagating disinformation. This includes the manipulation of images taken from reputable news resources. In response to these concerns and manipulations, we developed a web-based learning environment with a virtual learning companion (VLC). The VLC is designed to assist students in developing their critical thinking skills while interacting with social media content. The VLC is incorporated into a controlled learning environment that resembles Instagram and contains real and manipulated content. The “Courage” companion communicates with the students through chat dialogue and knowledge-activating questions. It also links additional images from web sources discovered through reverse image search based on image similarity. Learners are provided with various textual descriptions and keywords and are prompted to reflect on this information. The system’s effectiveness and assistance in identifying real or manipulated images have been evaluated in an empirical classroom study in August 2022 with 22 high school students in Germany that generated 95 conversations around five images and their texts assisted by the companion. Users’ interactions with the companion in a controlled Instagram-like social media environment have been recorded in xAPI format and then analyzed using a research dashboard. The results of this evaluation demonstrate that guidance through the VLC, along with the provision of additional content sources containing similar images and their explanation through the companion, improves the learners’ judgment. This corroborates the claim that the tool can help enhance learners’ critical thinking, resilience, and sensitivity facing social media issues.

A Black Box Comparison of Machine Learning Reverse Image Search for Cybersecurity OSINT Applications

Machine learning algorithms for reverse image search (a subset of open source intelligence or OSINT) provide a free, useful tool for determining the content of an image, where and when it was captured, and, in some cases, whether it has been digitally modified. Using a test data set of 24 images, we compared the performance of reverse image search for Google, Bing, and Yandex. Our black box experimental results are presented for three different categories of images (uncluttered images, images with significant background clutter, and facial recognition). The total number of correct images was highest for Google (65%), while Bing (55%) and Yandex (50%) yielded different results. Google was also the best at identifying cluttered, uncluttered, and facial images. We compare these results with previous studies and review how relative performance has changed over time. Accurate recognition rates for all reverse search platforms tested were higher for original images not previously uploaded, as opposed to images used in earlier studies. We validate our results using exchangeable image file format (EXIF) data and error level analysis (ELA) of selected images. Based on these results, best practices for OSINT image investigation are proposed.

Reverse Image Search for Collage: A Novel Local Feature-Based Framework

Reverse Image Search for Collage: A Novel Local Feature-Based Framework

Fighting cheapfakes: using a digital media literacy intervention to motivate reverse search of out-of-context visual misinformation

Abstract As a significant source of misinformation, out-of-context visual misinformation refers to visuals presented in an unrelated context. This study explores whether a digital media literacy intervention that features reverse image search tools has significant effects on participants’ message credibility judgment, discernment of visual misinformation, and intention of using reverse image search tools. Data were collected from a pre-registered, web-based, between-subjects experiment (N = 905). Results revealed a significant difference on intention of using reverse search tools among three experimental conditions: active intervention that involved both knowledge and behavior, passive intervention that involved knowledge only, and a control condition. Specifically, active intervention significantly increased intention of using reverse search tools, compared to the passive intervention and the control. Neither active nor passive intervention had an effect on credibility judgment or misinformation discernment. We discuss the implications for future digital media intervention designs and journalism practice that aim to combat visual misinformation.

Ecommerce Website with Recommendation System Including Chatbot and Reverse Image Search

Abstract: In Today’s generation, it has become a need to get clear vision, prioritize and effortlessly to provide appropriate information to decrease the problem of information overload, which has created a major problem for many users using internet. Recommender systems is capable to solve this major problem by using searching technique through a huge volume of dynamically generated useful data to provide users with personalized content and services depending on their interest. Recommendation system plays an important role in the internet world and is used in many applications. Recommendation systems are of three types: 1) collaborative filtering 2) content based and 3) hybrid based approach. With the advancement of machine learning for decades, there are still numerous problems insolvable, such as image recognition and location detection, image classification, image generation, speech recognition, natural language processing and so on. In this emerging field of deep learning, the research on topic like image classification has always been the topmost traditional research to be evolved. Simultaneously, Image recognition technology is also beneficial to gradually respond in better way to the development of international indicators, and betterment the development and progress in various fields. Hence, image processing technology which is based on machine learning has always been widely used in feature image segmentation, classification and recognition, and is an important topic in various fields. It allows you to find similar products in your store in a very easy way. Let the customer find the product based on the photo. What's more, you no longer have to manually match similar products, it will be done automatically by AI algorithm. The main purpose of this project is to use the concept of a machine learning algorithm to build a Recommendation and Reverse Image Search and recognition with the help of convolutional neural networks (CNN).

Content Based Reverse Image Search

People can now get access to the required image with a relevant degree of information thanks to the broad improvement of the WWW. Details, photos, flow charts, logos, maps, and other information However, discovering and obtaining relevant information is always a challenge. There are certain text-based search engines, such as Google, that may be used to find desired photographs from the vast pool of images available on the internet. As a result, here need for the pictures online search engine that can search for related and proper images. Content-based image retrieval query approach is same as Reverse image search that involves giving the CBIR device an example photo and having it find photos based on that image. Reverse image search mostly used to find out either information or photos linked to the query image, as well as accurate images.

Advanced Reverse Image Search and Profile Creation using Machine Learning

The Reverse image search is most widely used image search engine due to its extensive database that contains billions of images uploaded over the web. It is best to use the image search when your aim is to find identical images against your queried image. The reverse image search by image is idle option for people getting down similar images in different quality, size, or formats. This online facility leads us to explore image search results with a single click. A convolutional neural network is a type of artificial neural network used for image processing.

Reverse Image Querying

Abstract: As days go by, the volume of graphic i.e. visual data keeps increasing exponentially. This presents a more recent and complex set of problems for handling and making use of such vast, disorganised and unregulated data. Multiple organisations have launched many commercial products to tap into this market. There are multiple methods and functions for reverse image querying but most of them are either incomplete or compute-intensive to be a viable option for smaller use cases. In this study, we have implemented and improvised a rudimentary yet efficient solution by using the cosine similarity model to extract similar images for the given input image from the user. The model used is explained comprehensively together with visual representations. We have also explored multiple use cases that can be used as commercial products. The resulting web application can be utilised to act as a reverse image search engine as a standalone application or can be embedded as a submodule in a larger application.

Reverse Image Search Using Deep Unsupervised Generative Learning and Deep Convolutional Neural Network

Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. This paper proposes a novel deep learning-based methodology, which captures channel-wise, low-level details of each image. In the first phase, sparse auto-encoder (SAE), a deep generative model, is applied to RGB channels of each image for unsupervised representational learning. In the second phase, transfer learning is utilized by using VGG-16, a variant of deep convolutional neural network (CNN). The output of SAE combined with the original RGB channel is forwarded to VGG-16, thereby producing a more effective feature database by the ensemble/collaboration of two effective models. The proposed method provides an information rich feature space that is a reduced dimensionality representation of the image database. Experiments are performed on a hybrid dataset that is developed by combining three standard publicly available datasets. The proposed approach has a retrieval accuracy (precision) of 98.46%, without using the metadata of images, by using a cosine similarity measure between the query image and the image database. Additionally, to further validate the proposed methodology’s effectiveness, image quality has been degraded by adding 5% noise (Speckle, Gaussian, and Salt pepper noise types) in the hybrid dataset. Retrieval accuracy has generally been found to be 97% for different variants of noise

Comparative Analysis of Tineye and Google Reverse Image Search Engines

Comparative Analysis of Tineye and Google Reverse Image Search Engines

Visualizing Viral Cartography with MapReverse

Social media provide a fast-moving ecosystem for the rapid dissemination of digital maps. Some maps become viral, reaching massive audiences across diverse networks. A new challenge for cartographers is to characterize the design and dissemination of viral maps in social media. Here, we present a prototype visualization tool called MapReverse which allows users to explore collections of map images that are similar to a given viral map. Similar maps are identified using a combination of reverse image search and machine learning image analysis. In some cases, we can identify original sources for maps; more commonly we can identify many modifications of the original viral maps. This system provides a glimpse into the evolution of cartographic design as found in viral social media.

Exploring Google Reverse Image Search to Detect Visual Plagiarism in Interior Design

This study aims to explore the ability of Google Reverse Image Search (RIS) to detect plagiarism in images in the interior design field. Several image modifications were introduced by retaining the basic concept of the original image. These changes were classified into three categories as follow: a change in the design elements, introduced random changes by adding different objects to the existing image contents, and introduced various image effects. findings show that Google RIS does not take long to find newly uploaded images. Although it cannot detect changes related to the image contents, it can detect changes related to image size and contrast. Overall percentage of the modified images that were detected as matching the original image was only 5%. By contrast, the net percentage of images retrieved by Google RIS with contents actually related to the uploaded original image was 58.5%. Therefore, Google RIS is inaccurate in detecting any changes in the image contents irrespective of their simplicity, which implies that it cannot help in detection of visual plagiarism.

‘WHEN YOU REALISE YOUR DAD IS CRISTIANO RONALDO’: CELEBRITY SHARENTING AND CHILDREN'S DIGITAL IDENTITY

Sharing of parenthood experiences can be performed by ordinary users, to influencers and celebrities. This paper seeks to understand the digital identity of a child of a global celebrity through their presentation on social media, and to analyse qualitatively the visual formats of children’s representation in a platformised digital environment. It focuses on the representation of Cristiano Ronaldo’s children, especially his oldest son (Junior), through his Instagram account, his wife’s and his mother’s, in a two-year period. After identifying the main forms of representation of the children, we searched for images of Cristiano Junior across image search engines, social media platforms, sites and repositories; then, we used reverse image search for the identified memes. Ronaldo’s children are represented at home and family moments, playing and amidst luxury; 10-year-old Junior is presented as the ‘natural successor’ to Ronaldo’s talent for football, and also takes part in social or business events in public places. Visual representations created by Ronaldo’s sharenting are specially appropriated into memes that mock the football celebrity’s narcissism; or the fact that the child doesn’t know how to pose for selfies. Cristiano’s children’s digital identity and Junior’s, in particular, is populated with images as shared by parents and wider family, as well as constructed through participatory culture. An archaeological approach to visual social media content is not without difficulties, but proved to be fruitful to unpack the tensions of this wide visibility and the meanings it creates for the (digital) identity of the child.

Analysis of Perceptual Hashing Algorithms in Image Manipulation Detection

Perceptual image hashing is a family of algorithms that generate content-based image hashes. Unlike cryptographic hashes, perceptual hashes are designed to not change much when an image undergoes minor modifications such as compression, color-correction, and brightness. Therefore, these algorithms have been found useful in detecting redundant images, perform reverse image search, and flagging/filtering inappropriate imagery by comparing image hashes with a dataset of known perceptual hashes. While these applications have been served well, there is a new threat to visual information in the form of misinformation through content changing image manipulation using techniques such as object addition/removal and copy-move. While perceptual hashing algorithms can very accurately identify copies of similar images and their legitimately modified versions, their effectiveness in detecting content changing manipulations is not studied well. Existing research and advanced image libraries propose various image hashing algorithms, and several datasets of manipulated images are available in the public domain. In this work, a review and performance analysis of several perceptual hashing algorithms against two such datasets is presented. Results show less than ideal performance in distinguishing maliciously manipulated images from legitimate ones. Based on the results, limitations of perceptual hashing algorithms are identified, and future research directions are proposed.

Inaccuracy of Google reverse image search in complex dermatology cases

Inaccuracy of Google reverse image search in complex dermatology cases

STUDYING 'LIVE' CROSS-PLATFORM CIRCULATION OF IMAGES WITH A COMPUTER VISION API: AN EXPERIMENT BASED ON A SPORTS MEDIA EVENTd'Andréa, Carlos Frederico de Brito (Federal University of Minas Gerais, Brazil) <carlosfbd@gmail.com>

Considering the importance of cross-platform circulation of web contents for digital methods-oriented research, in this study we aim to expand the types of digital objects used as ‘traffic tags’ by focusing on static images as traces of online associations. We pursue this goal through a methodological experiment with Google Cloud Vision API, a computing framework for visual content analysis. Its Web Detection module pairs typical computer vision operations with Google’s search mechanism, partly performing as a more specialized batch reverse image search engine. This feature thus allows to potentially retrieve images’ spread across the web. We discuss the implications of this non-verbal methodological approach by tracking the 'live' cross-platform circulation of images shared on Twitter in the context of 2018 FIFA World Cup Final Draw ceremony, held on December 2017. Following a novel methodological protocol, we ran several iterations of Vision API processing, thus generating a time series of URLs pointing to pages in which images matching the ones being processed were found. The study analyses in depth the circulation of four popular images about the broadcasted media event, observing their ‘live’ spreading dynamics as well as the computer vision API performance. Among the findings, we point out the adoption of images as 'traffic tags' for cross-platform analysis as a promising approach to study web circulation beyond language barriers and mainstream platforms. Also, we find relevant data to discuss the specificities of the API’s algorithms and its opacity as inherent issues of the digital methods approach.

Challenges in Fighting Hate Speech, False Propaganda and Abuse on Twitter in Indian Context

The research highlights principle and specific challenges associated with the false propaganda, hate speech and abuse on twitter platform in Indian context. The paper speaks about 2 kind of challenges (1) Principle challenge and (2) Specific Challenges. Principle Challenges refer to comments expressed on the platform have varying influence on the users/ followers. Sometimes these have impact on their life and beliefs. More prominent when the content has ill informed facts and biased opinions (intentional or otherwise). Specific challenges refer to review of comments, Google reverse image search and using application programming interface to classify hate speech and abuse revealed key insights associated with limitations of the published algorithms. It also speaks about need for vigilance in regional sensitivities. The regional sensitivities of certain content and its implications on general perceptions could be really detrimental to the region in many cases (eg: Free Kashmir slogans or posters). While some of these challenges are being widely researched in a broader context, there is more specific need for India specific research. This research is intended to contributing to the growing community exhibiting need for regional or linguistic specific research. Besides, such studies could support the larger developer community to explore specific regional solutions.