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Studies have found that observers pay less attention to cast shadows in images than to better illuminated regions. In line with such observations, a recent study has suggested stronger change blindness for shadows than for objects (Ehinger et al., 2016). We here examine the role of (overt) visual attention in these findings by recording participants’ eye movements. Participants first viewed all original images (without changes). They then performed a change detection task on a subset of the images with changes in objects or shadows. During both tasks, their eye movements were recorded. In line with the original study, objects (subject to change in the change detection task) were fixated more often than shadows. In contrast to the previous study, better change detection was found for shadows than for objects. The improved change detection for shadows may be explained by the balancing of trials with object and shadow changes in the present study. Eye movements during change detection indicated that participants searched the bottom half of the images. Shadows were more often present in this region, which may explain why they were easier to find.

Rationale and Objectives Appropriate image manipulation of angiographic image display systems during interventional radiology is performed by radiological technologists and/or nurses given instructions from radiologists. However, appropriate images might not be displayed because of communication errors. Therefore, we developed a manipulation system that uses an eye tracker. The study aimed to determine if an angiographic image display system can be manipulated as well by using an eye tracker as by using a mouse. Materials and Methods An angiographic image display system using an eye tracker to calculate the gaze position on the screen and state of fixation was developed. Fourteen radiological technologists participated in an observer study by manipulating 10 images for each of 5 typical cases frequently performed in angiography, such as renal tumor, cerebral aneurysm, liver tumor, uterine bleeding, and hypersplenism. We measured the time from the start to the end of manipulating a series of images required when using the eye tracker and the conventional mouse. In this study, the statistical processing was done using Excel and R and R studio. Results The average time required for all observers for completing all cases was significantly shorter when using the eye tracker than when using the mouse (10.4 ± 2.1 s and 16.9 ± 2.6 s, respectively; p Conclusion Radiologists were able to manipulate an angiographic image display system directly by using the newly developed eye tracker system without touching contact devices, such as a mouse or angiography console. Therefore, communication error could be avoided.

Objective To give an overview of the available methods to investigate research misconduct in health-related research. Study Design and Setting In this scoping review, we conducted a literature search in MEDLINE, Embase, The Cochrane CENTRAL Register of Studies Online (CRSO), and The Virtual Health Library portal up to July 2020. We included papers that mentioned and/or described methods for screening or assessing research misconduct in health-related research. We categorized identified methods into the following four groups according to their scopes: overall concern, textual concern, image concern, and data concern. Results We included 57 papers reporting on 27 methods: two on overall concern, four on textual concern, three on image concern, and 18 on data concern. Apart from the methods to locate textual plagiarism and image manipulation, all other methods, be it theoretical or empirical, are based on examples, are not standardized, and lack formal validation. Conclusion Existing methods cover a wide range of issues regarding research misconduct. Although measures to counteract textual plagiarism are well implemented, tools to investigate other forms of research misconduct are rudimentary and labour-intensive. To cope with the rising challenge of research misconduct, further development of automatic tools and routine validation of these methods is needed. Trial registration number Center for Open Science (OSF) ( https://osf.io/mq89w ).

PurposeDigital collections are becoming more commonplace at libraries, archives and museums around the world, creating potential for improved accessibility to information that may otherwise remain hidden and further support for intellectual exploration. As a result of the growing potential for digital collections to inform and influence, the conversation surrounding ethics and digital collections needs to be continually examined and adapted as technologies evolve, user expectations change and digital information plays an increasing role in our everyday lives. In this context, this paper presents an overview of multifaceted ethical realities that impact the how, why and what digital information is created, accessed and preserved.Design/methodology/approachWritten from the perspective of a digital collections librarian, this paper relies on existing research in presenting ethical considerations and complements that research with professional observations in providing subsequent reflections on addressing challenges in the age of digital information.FindingsThere are and should be considerations given to not only what information is contained in a given collection, but also how that information is selected, accessed and consumed by the public. The conclusions offered are designed to provoke reflection on the evolving and interconnected nature of information and ethics in the context of digital collections.Originality/valueInformation ethics is multifaceted, with one of those facets relating directly to digital collections. This paper demonstrates that digital collections are more complex than simply a collection of digitized documents and photographs. As the field of information management continually evolves and adapts, so, too, do the ethical realizations identified in this paper, all of which go beyond the (virtual) walls of a library, archive or museum, and carry the potential to have a long-term impact concerning information and its integrity, equity and access.

Purpose A procedure is needed for bodies with disfiguring injuries to the face and the use of their portrait for visual identification. Method We present the application of a simple image processing procedure, otherwise known as ”bubbling,” which is based on the concept of ”perceptual filling-in,” to images for visual identification in the forensic context. The method is straight forward and can be performed using readily available software and hardware.. Results The method is demonstrated and examples are shown. The visual recognition of known persons using “bubbled” images was successfully tested. Conclusion The “bubbling” procedure for visual identification enhancement is quick and straightforward and may be attempted before escalating to more involved identification methods and procedures.

With the increasing visual realism of computer-graphics (CG) images generated by advanced rendering engines, the distinction between CG images and natural images (NIs) has become an important research problem in the image forensics community. Previous research works mainly focused on the conventional supervised learning framework, which usually requires a good quantity of labeled data for training. To our knowledge, we study, for the first time in the literature, the utility of the self-supervised learning mechanism for the forensic classification of CG images and NIs. The idea is to make use of a large number of readily available unlabeled data, along with a self-supervised training procedure on a well-designed pretext task for which labels can be generated in an automatic and convenient way without human manual labeling effort. Differing from existing self-supervised methods, based on pretext tasks targeted at image understanding, or based on contrastive learning, we propose carrying out self-supervised training on a forensics-oriented pretext task of classifying authentic images and their modified versions after applying various manipulations. Experiments and comparisons showed the effectiveness of our method for solving the CG forensics problem under different evaluation scenarios. Our proposed method outperformed existing self-supervised methods in all experiments. It could sometimes achieve comparable, or better, performance. compared with a state-of-the-art fully supervised method under difficult evaluation scenarios with data scarcity and a challenging forensic problem. Our study demonstrates the utility and potential of the self-supervised learning mechanism for image forensics applications.

A new style transfer-based image manipulation framework combining generative networks and style transfer networks is presented in this paper. Unlike conventional style transfer tasks, we tackle a new task, text-guided image manipulation. We realize style transfer-based image manipulation that does not require any reference style images and generate a style image from the user’s input sentence. In our method, since an initial reference input sentence for a content image can automatically be given by an image-to-text model, the user only needs to update the reference sentence. This scheme can help users when they do not have any images representing the desired style. Although this text-guided image manipulation is a new challenging task, quantitative and qualitative comparisons showed the superiority of our method.

Indigenous photobomb memes emerged on social networks as a media practice alongside contemporary activist campaigns, where artists insert pop culture content into digitized ethnographic photographs already in use as mainstream meme fodder. This article takes a materialist approach to such memes to explore how the technical processes of digital image editing function ideologically. Meme series by Kiowa-Choctaw artist Steven Paul Judd and others illustrate how compositing methods like cloning and healing tools may disrupt ethnographic photographs’ claims to history, interrogating the aesthetic systems that underwrite settler-colonial media. As these algorithmic processes remediate the digitized image and re-situate it relative to other photoshopping practices, they bear a trace of settler digitality that allows such memes to intervene in current cultural debates and aesthetic trends. Circulation via social media generates a web of twice-remediated memes, which always refer back to their prior analog and digital iterations.

BACKGROUND The aim of this study was to determine if image enhancement improves a clinician's ability to identify the presence of calculus on digital radiographs. METHODS Seventy-one hopeless teeth were collected from 34 patients. Teeth were stained with 1% methylene blue, the largest interproximal calculus deposit was scored, and photographs of each interproximal root surface were taken. The surface area of calculus deposit was determined as a percentage of the total interproximal root surface area. Digital radiographs of teeth taken before extraction were modified using the following enhancements: auto-contrast, emboss, invert, and sharpen. Radiographic presence of calculus was determined by two examiners. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each examiner and enhancement. A receiver operating characteristic curve was used to compare differences between the image enhancements in the detection of dental calculus. The kappa statistic was used to compare ratings between examiners. RESULTS None of the enhanced images were statistically superior to original images in identifying radiographic calculus (P > 0.05). The average sensitivity of digital radiography was 50%, average specificity was 82.2%, PPV was 94%, and NPV 23.2%. A threshold of >30% of interproximal root surface covered with calculus and increasing size of deposits were associated with improved detection (P < 0.05). CONCLUSIONS Digital enhancements do not significantly improve radiographic detection of dental calculus. As area of calculus on the root surface and size of calculus deposits increased, sensitivity of detection also increased.

Given a potentially manipulated probe image, provenance analysis aims to find all images derived from the probe (offsprings) and all images from which the probe is derived (ancestors) in a large dataset (provenance filtering), and reconstruct the manipulation history with the retrieved images (provenance graph building). In this paper, we address two major challenges in provenance analysis, retrieving the source image of the small regions that are spliced into the probe image, and, detecting source images within the search results. For the former challenge, we propose to detect spliced regions by pairwise image comparison and only use local features extracted from the spliced region to perform an additional search. This removes the influence of the background and greatly improves the recall. For the latter, we propose to learn a pairwise ancestor-offspring detector and use it jointly with a holistic image manipulation detector to identify the source image. The proposed provenance analysis system has performed remarkably in evaluations using comprehensive provenance datasets. It's the winning solution for NIST Media Forensics Challenge (MFC) in 2018, 2019 and 2020. In MFC 2019, our provenance results achieved a 12% improvement in filtering and a 20% gain in oracle provenance graphs building over the alternative methods. In the real-world Reddit dataset, the edge overlap between our reconstructed provenance graphs and the ground-truth graphs is 5 times better than the state-of-the-art system.

Deepfakes are a form of human image synthesis where an existing picture or image is superimposed into a video to change the identity of those depicted in the video. The technology relies on machine...

Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.

Misconduct in terms of manipulation of images has become an increasingly serious issue for the scientific community, especially in biomedicine. Such misconduct takes many forms, the major categories being falsified or fabricated images, manipulated images, and plagiarized images. Different tools and techniques are briefly described to help authors and editors in detecting such misconduct, and guidance is offered on appropriate use of images under different situations. More specifically, CrossRef Similarity Check, Motuin, and Droplets are proposed as the tools of choice for detecting similarity between images and their possible manipulation.

This article describes the structure of an implementation of a license plate identification system capable of reading the characters of a license plate from a digital image. It also collects the license plate detecting and character recognizing methods mentioned in the article, and we can read about rules regarding hungary's registered license plates as well. We can gain insight into the world of computer vision, as well as understand what image manipulation procedures a digital image must go through in order to extract characters from it. The system was written in Python and also used methods provided by OpenCV, as well as the Python-Tesseract optical character recognition tool to identify license plate characters.

A procedure is described for synthesizing an image with the same texture as a given input image. To achieve this, the output image is built up by successively adding pixels selected from the input image. Pixels are chosen by searching the input image for patches that closely match pixels already present in the output image. It is shown that the accurate reproduction of features in the input texture depends on the order in which pixels are added to the output image. A procedure for selecting an ordering which transfers large complex features of the input to the output image is described. This procedure is capable of reproducing large features even if only the interactions of nearby pixels are considered. The procedure can be altered to allow specification of the placement of particular features in the output texture. Several applications of this are described.

Multi-fragment rendering provides additional degrees of freedom in postprocessing. It allows us to edit images rendered with antialiasing, motion blur, depth of field, and transparency. To store multiple fragments, relationships between pixels and scene elements are often encoded into an existing image format. Most multi-fragment rendering systems, however, take into account only directly visible fragments on primary rays. The pixel coverage of indirectly visible fragments on reflected or refracted rays has not been well discussed. In this paper, we extend the generation of multiple fragments to support the indirect visibility in multiple bounces, which is often required by artists for image manipulation in productions. Our method is compatible with an existing multi-fragment image format such as Cryptomatte, and does not need any additional ray traversals during path tracing., Industry Track Papers, Atsushi Yoshimura, Yusuke Tokuyoshi, and Takahiro Harada

The image as a visual support of the lie, in parallel to its recognized capacity to constitute itself as evidence of the reality, has had a great social repercussion since its inception. This article aims to analyze the phenomenon of “fake news” in photojournalism and, specifically, in photography in the field of the mass media; very especially in the context of news agencies. The objective of the work has two aspects: on the one hand, to establish as an approximation, the tools and resources – taking into account their evolution – that have been used to construct false images from the moment of the invention of photography and, secondly, point out a possible typology of image manipulation according to the characteristics established, a common denominator, of the most prominent situations in the reference studies in the research carried out in this field. Criteria that will be applied to the study of a specific case, that of image manipulation in the Spanish Agency EFE. Work that shows the scarce photographic manipulation (photographic “fake news”) in the studied institution, allows to conclude the existence of quality in the agency’s photojournalistic information and confirms the social repercussion of the subjects of manipulation analyzed. La imagen como soporte visual de la mentira, en paralelo a su reconocida capacidad para constituirse como evidencia de lo real, ha tenido desde sus inicios una gran repercusión social. Este artículo pretende analizar el fenómeno de las “fake news” en el ejercicio del fotoperiodismo y, en concreto, en la fotografía en el ámbito de los medios de comunicación; muy especialmente en el contexto de las agencias de noticias. El objetivo del trabajo tiene dos vertientes: por un lado, establecer como aproximación las herramientas y recursos –teniendo en consideración su evolución- que se han empleado para construir imágenes falsas desde el mismo momento de la invención de la fotografía y, en segundo término, señalar una posible tipología de manipulación de la imagen según las características establecidas, como denominador común, de las situaciones más destacadas por los estudios de referencia en la investigación realizada en este ámbito. Criterios que serán aplicados al estudio de un caso concreto, el de la manipulación de la imagen en la española Agencia EFE. Trabajo que evidencia la escasa manipulación fotográfica (“fake news” fotográficas) en la institución estudiada, permite concluir la existencia de calidad en la información fotoperiodística de agencia y constata la repercusión social de los casos de manipulación analizados. &nbsp

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy–move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy–move forgery.

The use of preprints, manuscripts that can be uploaded to a public server and made almost immediately available for public dissemination without peer review, is becoming increasingly common.1 Preprint servers are not typically associated with established peer-reviewed journals and often operate independently. Proponents of preprints point toward improved rapid dissemination of results and opportunities for crowdsourced feedback before submission to peer-reviewed journals.1 Conversely, inconsistent upload criteria among preprint servers (such as lack of guidance for reporting conflict of interest or image manipulation),1 and the risk for widespread discourse of non-peer reviewed results by news media may impinge on research integrity.2 As members of the public may not understand the difference between preprints and traditionally peer-reviewed articles, preprints have the potential to cause widespread confusion and mistrust.2 Use of preprints may also make publication of results in traditional journals more difficult, as the results may be perceived to be less novel. Despite controversy, the number of preprints continue to rise.3 There is limited understanding of how dermatology journals view and consider preprints. In this study, we explore dermatology journal policies toward the submission of preprint articles for publication, and corresponding publication outcomes of dermatology articles previously uploaded to a large clinically oriented preprint server.

The monitoring of the intake os radionuclides by workers and public individuals, using in vivo measurements, requires the application of calibration factors obtained with physical-anthropomorphic phantoms. In the case of 131I, the lack of anatomical realism of the phantom might impair the quality and reliability of the monitoring result. Thus, in order to develop a thyroid phantom by means of 3D printing, images of the voxelized models provided in ICRP 110 were used. The superposition of the images of the original model and the treated model demonstrate the maintenance of morphological characteristics. Therefore, image manipulation techniques applied in this work aimed to smooth the sharp angles of the original image and the prototype printing were considered effective.

This chapter seeks to present a limited overview of some aspects of manipulated and/or fake images that contribute to society ‘becoming post-truth’. It subclassifies levels of manipulation and also presents the finding from a descriptive survey that gauges perceptions on awareness and recognisability of fake images. It also presents perceptions of effect on individuals of images modified for aesthetic reasons and carried by social media. The majority of respondents seemed affected by this, but with only a minority whose perception of self was affected. Another result of the survey is that there is a general mistrust of images not carried by gatekept sources.

State-of-the-art multi-stream methods for image manipulation detection suffer from gaps between features. Moreover, the rapid development of GANs makes it an emerging method of image tampering. However, existing natural scene image tampered datasets are limited to manual tampering. In this paper, we address these two issues. Firstly, we propose a novel two-stream multi-modality image manipulation detection model (MM-net) that abandons the way of fusion. The main idea of the proposed model is to exploit one stream to guide the learning of the other stream through attention mechanism. Therefore, our approach enjoys the benefits of the multi-stream methods while avoiding the semantic gaps caused by bridging gaps between different streams. Secondly, we build the first tampered dataset of natural images based on GANs, pushing manipulation detection toward more realistic and challenging scenarios. Extensive experimental results demonstrate that our model outperforms state-of-the-art approaches on both manually and GANs tampered images.

We review the phenomenon of deepfakes, a novel technology enabling inexpensive manipulation of video material through the use of artificial intelligence, in the context of today’s wider discussion on fake news. We discuss the foundation as well as recent developments of the technology, as well as the differences from earlier manipulation techniques and investigate technical countermeasures. While the threat of deepfake videos with substantial political impact has been widely discussed in recent years, so far, the political impact of the technology has been limited. We investigate reasons for this and extrapolate the types of deepfake videos we are likely to see in the future.

I have always been interested in geometry. When I was about 12 years old, my grandfather Oskar Stampfli, an excellent teacher and mathematician, showed me the tilings of the plane with regular poly...

Introduction Significant advances in three-dimensional (3D) imaging technology have allowed for the incorporation of 3D digital displays into medical and surgical devices. Despite initial adoption of the NGENUITY® 3D Visualization System in vitreoretinal surgery, there are limited publications regarding its use. The generally accepted main benefits include improved ergonomics, enhanced surgical team communication and education, reduced retinal phototoxicity, increased depth of field, and display image manipulation. Despite these potential benefits, many retina specialists have questioned its universal applicability to a wide variety of vitreoretinal surgeries. Objective To report on the variety of indications and surgical efficacy of the NGENUITY® 3D Visualization System in vitreoretinal surgery via a review of surgical experience at two vitreoretinal practices in both the academic and community settings. Methods A retrospective review was conducted of consecutive surgical cases performed on the NGENUITY® 3D Visualization System at Massachusetts Eye and Ear Infirmary and Florida Retina Institute from June 1st, 2017 to November 1st, 2018. Age, presenting diagnosis, surgical procedure, and intraoperative details were recorded. Results 272 vitreoretinal surgeries on the Alcon NGENUITY® 3D Visualization System were identified between June 1st, 2017 and November 1st, 2018 at the participating institutions. A detailed breakdown of the indications for surgery and related procedures is reported. During all 272 cases on the 3D digital system, there were no complications attributed to the visualization system. Conclusion This series illustrates the diversity of vitreoretinal surgeries that can be performed on this system without compromising surgical viewing or increasing surgical complications. The Alcon NGENUITY® 3D Visualization System possesses favorable ergonomics, illumination levels, depth of field, display filters, and trainee experience.

This work discusses the development of an augmented reality (AR) interface that allows users to manipulate 3D anatomical structures (segmented and reconstructed from real medical studies) through haptic interactions by means of a five-degrees-of-freedom hand-exoskeleton. The exoskeleton allows the hand to move throughout its nominal range of motion. Our interface allows users to manipulate virtual objects more naturally thanks to its three points of contact, unlike most commercial haptic systems, which only have one point of contact. The preliminary results indicated a proper 3D segmentation and reconstruction of the anatomical structures of interest, as well as an adequate interaction between the haptic exoskeleton and the AR interface.

Facial image manipulation is a particular instance of digital image tampering, which is done by compositing a region from one facial image into another facial image. Fake images generated by facial image manipulation now spread like wildfire on news websites and social networks, and are considered the greatest threat to press freedom. Previous research relied heavily on handcrafted features to analyze tampered regions which were inefficient and time-consuming. This paper introduces a framework that accurately detects manipulated face image using deep learning approach. The original contributions of this paper include (1) a customized convolutional neural network model for Manipulated Face (MANFA) identification; it contains several convolutional layers that effectively extract features of multi-levels of abstraction from a tampered region. (2) A hybrid framework (HF-MANFA) that uses Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) to deal with the imbalanced dataset challenge. (3) A large manipulated face dataset that is manually collected and validated. The results from various experiments proved that proposed models outperformed existing expert and intelligent systems which were usually used for the manipulated face image detection task in terms of area under the curve (AUC), computational complexity, and robustness against imbalanced datasets. As a result, the presented framework will motivate the finding of a more powerful altered face images detection method and encourages the integration of the proposed model in applications that have to deal with manipulated images regularly.

In forensic intelligence-gathering, footprints have been shown to be valued evidence found at crime scenes. Forensic podiatrists and footprint examiners use a variety of techniques for measuring footprints for comparison of the crime scene evidence with the exemplar footprints. This study examines three different techniques of obtaining two-dimensional linear measurement data of dynamic bare footprints. Dynamic bare footprints were gathered from 50 students from a podiatric medical school using the Identicator® Inkless Shoe Print Model LE 25P system. After obtaining 100 bilateral footprints from the participants, the quantitative measurement data were collected by using three different measurement techniques: (i) a manual technique using a ruler (direct technique); (ii) an Adobe® Photoshop® technique; and (iii) a GIMP (GNU Image Manipulation Program) technique. The seven Reel linear measurement methodology was used for producing measurements using these three techniques. This study showed that all the mean bare footprint measurements on the right and left feet obtained using the direct technique were larger than those obtained using GIMP and Adobe® Photoshop® images. Differences were also observed in measurements produced using GIMP software and Photoshop images. However, the differences observed in the three techniques used for bare footprint measurements were not found to be statistically significant. The study concludes that there are no significant differences between the three measurement techniques when applied to two-dimensional bare footprints using the Reel method. It further concluded that any of these measurement techniques can be used when employing the Reel methodology for footprint analysis without significant difference.

The increasing sophistication of photo-editing software means that even amateurs can create compelling doctored images. Yet recent research suggests that people’s ability to detect image manipulations is limited. Given the prevalence of manipulated images in the media, on social networking sites, and in other domains, the implications of mistaking a fake image as real, or vice versa, can be serious. In seven experiments, we tested whether people can make use of errors in shadows and reflections to determine whether or not an image has been manipulated. Our results revealed that people’s ability to identify authentic and manipulated scenes based on shadow and reflection information increased with the size of the manipulation, but overall, detection rates remained poor. Consistent with theories of incomplete visual representation, one possible reason for these findings could be that people rarely encode the details of scenes that provide useful cues as to the authenticity of images. Overall, our findings indicate that people do not readily make use of shadow and reflection cues to help determine the authenticity of images—yet it remains possible that people could make use of these cues, but they are simply unaware of how to do so.

Multi-purpose forensics is attracting increasing attention worldwide. In this paper, we propose a multi-purpose method based on densely connected convolutional neural networks (CNNs) for simultaneous detection of 11 different types of image manipulations. An efficient CNN structure has been specifically designed for forensics by considering vital architecture components, including the number of convolutional layers, the size of convolutional kernels, the nonlinear activations, and the type of pooling layer. The dense connectivity pattern, which has better parameter efficiency than the traditional pattern, is explored to strengthen the propagation of features related to image manipulation detection. When compared with four state-of-the-art methods, our experiments demonstrate that the proposed CNN architecture can achieve better performance in multiple operation detections for different image sizes, especially on small image patches. Consequently, the proposed method can accurately detect local image manipulations. The proposed method can achieve better overall performance when tested on different databases as well as better robustness against JPEG compression even under low-quality JPEG compression.

This position paper addresses three areas in which photographic images are used and abused: Journalism, Advertising, and Public Relations. These areas do not use images for aesthetic purposes only; for these businesses, the images are more than mental impressions. In these three industries, the messages they deliver through the images have little to do with the objective reality. All three use images to deliver an opinion in the name of journalism, distorted needs, benefits, and solutions in advertising, and put spins on facts to present favorable images of their clients in Public Relations. Historically, all three have engaged in the unethical use of the concept “image making” and of manipulation of actual images.

Encryption-then-Compression (EtC) systems have been proposed to securely transmit images through an untrusted channel provider. In this study, EtC systems were applied to social media like Twitter that carry out image manipulations. The block scrambling-based encryption schemes used in EtC systems were evaluated in terms of their robustness against image manipulation on social media. The aim was to investigate how five social networking service (SNS) providers, Facebook, Twitter, Google+, Tumblr and Flickr, manipulate images and to determine whether the encrypted images uploaded to SNS providers can avoid being distorted by such manipulations. In an experiment, encrypted and non-encrypted JPEG images were uploaded to various SNS providers. The results show that EtC systems are applicable to the five SNS providers., Comment: To be appeared in IEICE Transactions on Information and Systems, January 2019

Aim/Purpose: In this paper, we address the theoretical challenges today’s scientific community faces to precisely draw lines between true and false pictures. In particular, we focus on problems related to the hidden wonders of science and the shiny images produced for scientific papers or to appeal to wider audiences. Background: As rumors (hoaxes) and false news (fake news) explode across society and the current network, several initiatives using current technology have been launched to study this phenomena and limit the social impact. Over the last two decades, inappropriate scientific behavior has raised more questions about whether some scientific images are valid. Methodology: This work is not about analyzing whether today’s images are objective. Instead, we advocate for a general approach that makes it easier to truly believe in all kinds of knowledge, scientific or otherwise (Goldman, 1967; Goldman, & Olson, 2009). This need to believe is closely related to social order (Shapin, 1994). Contribution: We conclude that we must ultimately move away from older ideas about truth and objectivity in research to broadly approach how science and knowledge are represented and move forward with this theoretical approach when communicating science to the public. Findings: Contemporary visual culture suggests that our world is expressed through images, which are all around us. Therefore, we need to promote the reliability of scientific pictures, which visually represent knowledge, to add meaning in a world of complex high-tech science (Allamel-Raffin, 2011; Greenberg, 2004; Rosenberger, 2009). Since the time of Galileo, and today more than ever, scientific activity should be understood as knowledge produced to reveal, and therefore inform us of, (Wise, 2006) all that remains unexplained in our world, as well as everything beyond our senses. Recommendation for Researchers: In journalism, published scientific images must be properly explained. Journalists should tell people the truth, not fake objectivity. Today we must understand that scientific knowledge is mapped, simulated, and accessed through interfaces, and is uncertain. The scientific community needs to approach and explain how knowledge is represented, while paying attention to detail. Future Research: In today’s expanding world, scientific research takes a more visual approach. It is important for both the scientific community and the public to understand how the technologies used to visually represent knowledge can account for why, for example, we know more about electrons than we did a century ago (Arabatzis, 1996), or why we are beginning to carefully understand the complexities and ethical problems related to images used to promote knowledge through the media (see, i.e., López-Cantos, 2017).

Deepfake image manipulation has become a serious security threat to the social network. Currently, there are limited studies on protective methods that are against Deepfake image manipulation. To tackle this problem, we here propose an adversary attack based smart watermark model, which adds unperceptive watermarks to images so that the images become adversary examples to Deepfake models. When the Deepfake manipulates these watermarked images, the manipulated images become blur. The manipulation thus can be easily recognized by human and machines. Our experiments have shown that our model outperforms the SOTA and can be used to effectively prevent Deepfake manipulation.

Scientific illustration continues to remain a critical part of taxonomy. Illustrations often require lots of time and, in many cases, the results are not as expected. At present, taxonomy journals only accept high quality digital illustrations; thus, image manipulation programs using vector or bitmap graphics have become the new focus of attention. This paper provides a step-by-step guide to making illustrations using bitmap graphics in Autodesk SketchBook. This application provides an alternative to other known tools by allowing: 1) faster illustrations; 2) direct drawing with a wide range of tools that simulate traditional drawing; 3) more detailed illustrations; and 4) an easy interface and work-flow for novice illustrators, all while being completely free and compatible with multiple operating systems.

In this paper, we propose a novel CycleGAN without checkerboard artifacts for counter-forensics of fake-image detection. Recent rapid advances in image manipulation tools and deep image synthesis techniques, such as Generative Adversarial Networks (GANs) have easily generated fake images, so detecting manipulated images has become an urgent issue. Most state-of-the-art forgery detection methods assume that images include checkerboard artifacts which are generated by using DNNs. Accordingly, we propose a novel CycleGAN without any checkerboard artifacts for counter-forensics of fake-mage detection methods for the first time, as an example of GANs without checkerboard artifacts.