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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.

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.

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.

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.

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

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.

The recent and rapid growth in image manipulation techniques raises grave concerns with respect to document forgery. It also diminishes trust in the data available publicly and can cause harm by spreading false information. In this paper, the problem of facial forgery by training a neural network to distinguish between real and fake faces has been addressed. The convolutional neural network has been trained with the help of transfer learning, which allows us to utilize computer vision models like ResNet and AlexNet, which were pre-trained on massive datasets. The network was evaluated on a dataset of 2041 images provided by the computational Intelligence and Photography Lab at the Department of Computer Science, Yonsei University. Results show that ResNet-152 is able to detect facial forgery with an accuracy of up to 76.79%.

Satellite imagery is becoming increasingly accessible due to the growing number of orbiting commercial satellites. Many applications make use of such images: agricultural management, meteorological prediction, damage assessment from natural disasters, or cartography are some of the examples. Unfortunately, these images can be easily tampered and modified with image manipulation tools damaging downstream applications. Because the nature of the manipulation applied to the image is typically unknown, unsupervised methods that don't require prior knowledge of the tampering techniques used are preferred. In this paper, we use ensembles of generative autoregressive models to model the distribution of the pixels of the image in order to detect potential manipulations. We evaluate the performance of the presented approach obtaining accurate localization results compared to previously presented approaches.

Image data augmentation is a way that makes it possible to increase the diversity of available data without actually collecting new data. In this study, researchers have evaluated the application of image manipulation with the Thatcher effect, double illusion, and inversion on the performance of face detection for data augmentation needs where the data obtained has a weakness that is the limited amount of data to create a training model. The purpose of this research is to increase the diversity of the data so that it can make predictions correctly if given other similar datasets. To perform face detection on images, it is done using YOLOv3 then comparing the accuracy results from the dataset after and before adding data augmentation.

Image forgery is an active topic in digital image tampering that is performed by moving a region from one image into another image, combining two images to form one image, or retouching an image. Moreover, recent developments of generative adversarial networks (GANs) that are used to generate human facial images have made it more challenging for even humans to detect the tampered one. The spread of those images on the internet can cause severe ethical, moral, and legal issues if the manipulated images are misused. As a result, much research has been conducted to detect facial image manipulation based on applying machine learning algorithms on tampered face datasets in the last few years. This paper introduces a deep learning-based framework that can identify manipulated facial images and GAN-generated images. It is comprised of multiple convolutional layers, which can efficiently extract features using multi-level abstraction from tampered regions. In addition, a data-based approach, cost-sensitive learning-based approach (class weight), and ensemble-based approach (eXtreme Gradient Boosting) is applied to the proposed model to deal with the imbalanced data problem (IDP). The superiority of the proposed model that deals with an IDP is verified using a tampered face dataset and a GAN-generated face dataset under various scenarios. Experimental results proved that the proposed framework outperformed existing expert systems, which has been used for identifying manipulated facial images and GAN-generated images in terms of computational complexity, area under the curve (AUC), and robustness. As a result, the proposed framework inspires the development of research on image forgery identification and enables the potential to integrate these models into practical applications, which require tampered facial image detection.

Learning interpretable data representation has been an active research topic in deep learning and computer vision. While representation disentanglement is an effective technique for addressing this task, existing works cannot easily handle the problems in which manipulating and recognizing data across multiple domains are desirable. In this paper, we present a unified network architecture of Multi-domain and Multi-modal Representation Disentangler ( $M^{2}RD$ ), with the goal of learning domain-invariant content representation with the associated domain-specific representation observed. By advancing adversarial learning and disentanglement techniques, the proposed model is able to perform continuous image manipulation across data domains with multiple modalities. More importantly, the resulting domain-invariant feature representation can be applied for unsupervised domain adaptation. Finally, our quantitative and qualitative results would confirm the effectiveness and robustness of the proposed model over state-of-the-art methods on the above tasks.

Various image editing tools make our pictures more attractive, and at the same time, evoke different emotional responses. With powerful and easy-to-use imaging applications, capturing, editing and then sharing pictures have become daily life for many. This paper investigates the influence of several image manipulations on evoked emotions for different types of images. To do so, various types of images clustered in different categories, were collected from Instagram and subjective evaluations were conducted via crowdsourcing to gather the emotional responses on different manipulations as perceived by subjects. Evaluation results show that certain image manipulations can induce different evoked emotions on transformed pictures when compared to the original ones. However, such changes in image emotions due to manipulation are highly content dependent. Then, we conducted a machine learning based experiment, in attempt to predict the emotions of a manipulated image given its original version and the desired manipulation method. Experimental results present a promising performance of such a prediction model, which could pave the road to automatic selection or recommendation of image editing tools that can efficiently transform or emphasize desired emotions in pictures.

Particleboards may exhibit several defect types caused by a variety of sources during the manufacturing process. It is essential to quickly determine when a defect is present and localize the fault so that the board can either be fixed or discarded. Several methods have been already been developed to address this issue to varying degrees of success. In this work, a novel process is presented which quickly determines whether a defect exists or not using traditional machine learning techniques on a bivariate color histogram of the particleboard and then localize the defect using automated image manipulation techniques. The workflow of quickly determining if a defect is present then using a more computationally intensive technique to localize and classify the defect can be extended to use other methods or even to other processes.

Recent works in computer vision and multimedia have shown that image memorability can be automatically inferred exploiting powerful deep-learning models. This article advances the state of the art in this area by addressing a novel and more challenging issue: “ Given an arbitrary input image, can we make it more memorable? ” To tackle this problem, we introduce an approach based on an editing-by-applying-filters paradigm: given an input image, we propose to automatically retrieve a set of “style seeds,” i.e., a set of style images that, applied to the input image through a neural style transfer algorithm, provide the highest increase in memorability. We show the effectiveness of the proposed approach with experiments on the publicly available LaMem dataset, performing both a quantitative evaluation and a user study. To demonstrate the flexibility of the proposed framework, we also analyze the impact of different implementation choices, such as using different state-of-the-art neural style transfer methods. Finally, we show several qualitative results to provide additional insights on the link between image style and memorability.

The widespread availability of image editing tools and improvements in image processing techniques allow image manipulation to be very easy. Oftentimes, easy-to-use yet sophisticated image manipulation tools yields distortions/changes imperceptible to the human observer. Distribution of forged images can have drastic ramifications, especially when coupled with the speed and vastness of the Internet. Therefore, verifying image integrity poses an immense and important challenge to the digital forensic community. Satellite images specifically can be modified in a number of ways, including the insertion of objects to hide existing scenes and structures. In this paper, we describe the use of a Conditional Generative Adversarial Network (cGAN) to identify the presence of such spliced forgeries within satellite images. Additionally, we identify their locations and shapes. Trained on pristine and falsified images, our method achieves high success on these detection and localization objectives., Accepted to the 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)

The task of synthesizing sketches from photographs has been pursued with image processing methods and supervised learning based approaches. The former lack flexibility and the latter require large quantities of ground-truth data which is hard to obtain because of the manual effort required. We present a convolutional neural network based framework for sketch generation that does not require ground-truth data for training and produces various styles of sketches. The method combines simple analytic loss functions that correspond to characteristics of the sketch. The network is trained on and evaluated for human face images. Several stylized variations of sketches are obtained by varying the parameters of the loss functions. The paper also discusses the implicit abstraction afforded by the deep convolutional network approach which results in high quality sketch output.

This article discusses image manipulation in the context of digital collections at academic and cultural heritage institutions. The nature of this manipulation is not intended to be deceptive or misleading. Instead it aids users in accessing information present in visual material collections. Although well intended, the manipulation of digital objects does beg some ethical considerations. In its discussion of image manipulation, this article highlights common approaches to image manipulation that may already be present in many digital collections and it is aimed at starting a larger conversation about post-processing standardization.

Currently the number of cameras embedded in mobile devices is growing at an unprecedented rate. Additionally, the quality and performance of these mobile cameras is steadily improving, and is closing in on that of classical digital cameras. This scenario makes the forensic analysis of images taken with mobile cameras increasingly important and necessary. Among the various branches of forensic analysis, this paper focuses on the reliable acquisition of the make and model of the mobile camera that produced a given image. For this we have developed a technique based on exchangeable image file format (Exif) metadata analysis, allowing us in certain cases to obtain both the make and model with which the photo was taken. This comes with considerable analysis of whether this metadata information could be trusted, and with additional tools that can help in discovering image manipulation. These and other capabilities have been integrated into a new tool we have developed called Theia, that also offers many other advantages to the forensic analyst that has to mass process and analyze thousands of images in the fastest and most forensically sound way. To that end, we have also incorporated various complex functions that greatly help the forensic analyst, such as different types of advanced queries on Exif metadata information of large sets of images, and advanced geopositioning capabilities.

A large number of digital photos are being generated and with the help of advanced image editing software and image altering tools, it is very easy to manipulate a digital image nowadays. These manipulated or tampered images can be used to delude the public, defame a person's personality and business as well, change political views or affect the criminal investigation. The raw image can be mutilated in parts or as a whole image so there is a need for detection of what type of image tampering is performed and then localize the tampered region. Initially, single handcrafted manipulated images were used to detect the only image tampering present in the image but in a real-world scenario, a single image can be mutilated by numerous image manipulation techniques. Nowadays, multiple tampering operations are performed on the image and post-processing is done to erase the traces left behind by the tampering operation, making it more difficult for the detector to detect the tampering. It is seen that the recent techniques that are used to detect image manipulation are based on deep learning methods. In this paper, more focus is on the study of various recent image manipulation detection techniques. We have examined various image forgeries that can be performed on the image and various image manipulation detection and localization methods.

In recent years, convolutional neural networks (CNNs) have been widely used by researchers to perform forensic tasks such as image tampering detection. At the same time, adversarial attacks have been developed that are capable of fooling CNN-based classifiers. Understanding the transferability of adversarial attacks, i.e. an attacks ability to attack a different CNN than the one it was trained against, has important implications for designing CNNs that are resistant to attacks. While attacks on object recognition CNNs are believed to be transferrable, recent work by Barni et al. has shown that attacks on forensic CNNs have difficulty transferring to other CNN architectures or CNNs trained using different datasets. In this paper, we demonstrate that adversarial attacks on forensic CNNs are even less transferrable than previously thought even between virtually identical CNN architectures! We show that several common adversarial attacks against CNNs trained to identify image manipulation fail to transfer to CNNs whose only difference is in the class definitions (i.e. the same CNN architectures trained using the same data). We note that all formulations of class definitions contain the unaltered class. This has important implications for the future design of forensic CNNs that are robust to adversarial and anti-forensic attacks.

Systems that perform image manipulation using deep convolutional networks have achieved remarkable realism. Perceptual losses and losses based on adversarial discriminators are the two main classes of learning objectives behind these advances. In this work, we show how these two ideas can be combined in a principled and non-additive manner for unaligned image translation tasks. This is accomplished through a special architecture of the discriminator network inside generative adversarial learning framework. The new architecture, that we call a perceptual discriminator, embeds the convolutional parts of a pre-trained deep classification network inside the discriminator network. The resulting architecture can be trained on unaligned image datasets, while benefiting from the robustness and efficiency of perceptual losses. We demonstrate the merits of the new architecture in a series of qualitative and quantitative comparisons with baseline approaches and state-of-the-art frameworks for unaligned image translation.

GIMP is a high-quality, full-featured, image manipulation program. The program is free, but it comes with minimal documentation. This is both good and bad. It’s bad if you want to use the more advanced features, but it’s good for authors who write books like this one to fill the documentation gap.

This paper investigates impacts of digital technologies on photographic post-production and image manipulation in information photography. An attempt is made to assess the extent to which ethical codes and conventions are being modified concerning the digital treatment of photographic images. We sought to assess whether one can identify, in the rules of photographic competitions, an embryonic process of establishment of new boundaries as to what constitutes content manipulation and what is acceptable as technical adjustments. We observed that, in a large proportion of cases, competition rules consist of very generic guidelines regarding the acceptable procedures for photo editing, without clear distinctions between technical adjustment and manipulation. Despite such limitations, we conclude that the analysis of photographic competitions can lead to identification of observable regularities that may act as ethical standards in relation to post-production images.

There are several types of photo manipulation, one of them is splicing manipulation. The problem was nowadays the image manipulation software is used by the person who has not an expert may manipulate the photo. The question was how we can be sure whether the photo is original or not. Therefore, this paper tested the photo splicing detection using two methods: error level analysis and laplacian-edge detection by the plugin of GIMP. The result was to detect photo splicing that has several variables which may enable the method to work better, i.e., the characteristic of the camera and the JPEG compression that characterized by the camera. This experiment and analysis can be used as a reference for photo forensics investigation.

When creating a forgery, a forger can modify an image using many different image editing operations. Since a forensic examiner must test for each of these, significant interest has arisen in the development of universal forensic algorithms capable of detecting many different image editing operations and manipulations. In this paper, we propose a universal forensic approach to performing manipulation detection using deep learning. Specifically, we propose a new convolutional network architecture capable of automatically learning manipulation detection features directly from training data. In their current form, convolutional neural networks will learn features that capture an image's content as opposed to manipulation detection features. To overcome this issue, we develop a new form of convolutional layer that is specifically designed to suppress an image's content and adaptively learn manipulation detection features. Through a series of experiments, we demonstrate that our proposed approach can automatically learn how to detect multiple image manipulations without relying on pre-selected features or any preprocessing. The results of these experiments show that our proposed approach can automatically detect several different manipulations with an average accuracy of 99.10%.

Have you ever taken a picture only to find out that an unimportant background object ended up being overly salient? Or one of those team sports photos where your favorite player blends with the rest? Wouldn't it be nice if you could tweak these pictures just a little bit so that the distractor would be attenuated and your favorite player will stand-out among her peers? Manipulating images in order to control the saliency of objects is the goal of this paper. We propose an approach that considers the internal color and saliency properties of the image. It changes the saliency map via an optimization framework that relies on patch-based manipulation using only patches from within the same image to achieve realistic looking results. Applications include object enhancement, distractors attenuation and background decluttering. Comparing our method to previous ones shows significant improvement, both in the achieved saliency manipulation and in the realistic appearance of the resulting images., Comment: to appear in WACV'18

The advent of sophisticated photo editing software has made it increasingly easier to manipulate digital images. Often visual inspection cannot definitively distinguish the resulting forgeries from authentic photographs. In response, forensic techniques have emerged to detect geometric or statistical inconsistencies that result from specific forms of photo manipulation. In this article we describe a new forensic technique that focuses on geometric inconsistencies that arise when fake reflections are inserted into a photograph or when a photograph containing reflections is manipulated. This analysis employs basic rules of reflective geometry and linear perspective projection, makes minimal assumptions about the scene geometry, and only requires the user to identify corresponding points on an object and its reflection. The analysis is also insensitive to common image editing operations such as resampling, color manipulations, and lossy compression. We demonstrate this technique with both visually plausible forgeries of our own creation and commercially produced forgeries.

Ada provides good support for the implementation of dependable, real-time, control systems. However, its support for other styles of systems is not as good. This paper explores the support available for implementing parallel, stream based systems. The paper presents an implementation of an image manipulation system which highlights deficiencies in the support for such systems in the Ada language. Two additional semantics are proposed for addition to the Ada language which will provide for the needs of these systems. The broadcast semantic allows the same data to be written to several POs simultaneously. The guarded protected function semantic permits several readers to wait on an entry and simultaneously read data from the PO.

Rationale and Objectives: To identify parameters linked to higher levels of performance in screening mammography. In particular we explored whether experience in reading digital cases enhances radiologists’ performance. Methods: A total of 60 cases were presented to the readers, of which 20 contained cancers and 40 showed no abnormality. Each case comprised of four images and 129 breast readers participated in the study. Each reader was asked to identify and locate any malignancies using a 1-5 confidence scale. All images were displayed using 5MP monitors, supported by radiology workstations with full image manipulation capabilities. A jack-knife free-response receiver operating characteristic, figure of merit (JAFROC, FOM) methodology was employed to assess reader performance. Details were obtained from each reader regarding their experience, qualifications and breast reading activities. Spearman and Mann Whitney U techniques were used for statistical analysis. Results: Higher performance was positively related to numbers of years professionally qualified (r= 0.18; P Conclusion: This study suggests suggestion that readers with experience in digital images reporting may exhibit a reduced ability to correctly identify normal appearances requires further investigation. Higher performance is linked to number of cases read per year.

Gül, Özge (Dogus Author) Computer Aided Design (CAD) today plays an increasingly important role for interior architecture. 25 years ago, almost all interior architecture presentations are drawn on a paper with a pencil. Today CAD radically changes all these traditional drawing habits and it becomes an economical and technological requirement for interior architecture profession.In this study, the instructional methods used in CAD courses are analyzed according to "CIDA 2014 Student Learning Expectations" through student surveys. At the end of the study, it is evaluated which instructional methods are more beneficial to teach "Computer Aided Presentation and Image Manipulation Techniques" in Interior Architecture Education.

In this paper, we present a unified understanding on the formal performance evaluation for image manipulation forensics techniques. With hypothesis testing model, security is qualified as the difficulty for defeating an existing forensics system and making it generate two types of forensic errors, i.e., missing and false alarm detection. We point out that the security on false alarm risk, which is rarely addressed in current literatures, is equally significant for evaluating the performance of manipulation forensics techniques. With a case study on resampling-based composition forensics detector, both qualitative analyses and experimental results verify the correctness and rationality of our understanding on manipulation forensics security.

Currently, computer vision applications are becoming more common on mobile devices due to the constant increase in raw processing power coupled with extended battery life. The OpenCV framework is a popular choice when developing such applications on desktop computers as well as on mobile devices, but there are few comparative performance studies available. We know of only one such study that evaluates a set of typical OpenCV operations on iOS devices. In this paper we look at the same operations, spanning from simple image manipulation like grayscaling and blurring to keypoint detection and descriptor extraction but on flagship Android devices as well as on iOS devices and with different image resolutions. We compare the results of the same tests running on the two platforms on the same datasets and provide extended measurements on completion time and battery usage.

Multidisciplinary collaboration is a key requirement in several contemporary interventional radiology procedures (IRPs). We proposed a hybrid system (NetAngio) to enable "on the fly" heterogeneous collaboration to support IRP providing intraoperating essential services, and investigate its feasibility and effectiveness in a referral medical center. We have developed a Web-based, cost-effective structure, able to support real-time mentoring, image manipulation, and education services beyond the boundaries of the single institution and potentially allow sub specialists to participate in opinion and decision making in more complex cases. Supported services based on a Motion Joint Photographic Experts Group (MJPEG) coder/decoder (CODEC) can be easily accessible by authorized collaborators, within a user-friendly interface by using a typical Web-browser. Ten interventional radiologists, two vascular surgeons and two medical physicists participated in 33 "fully collaborative" cases during a 13-month period from January 2004 to February 2005. In addition, fifteen 90-minute open seminars and finally, 75 expert's module activations, and 255 learner's module activations were performed during the evaluation period. Collaborative procedures are able to enhance outcomes performance especially in more complex cases where the simultaneous presence of a remote expert interventionist and a medical physicist or a surgeon is required. Further research is needed to promote integration of additional data sources and services.

Despite the fact that pedagogical programming environments have been available for several years, some fundamental concepts of object-oriented programming and design, such as encapsulation, interfaces, polymorphism, and inheritance, are not addressed by existing tools to a full extent. This paper presents novel interaction metaphors embodied in tool support that, as opposed to existing tools, makes possible to illustrate such object-oriented programming concepts with first-class representations. A tool referred to as AguiaJ, which is being used at our institution for the past three years, embodies novel interaction metaphors for addressing the aforementioned concepts in a gradual and comprehensive way. We explain the innovative tool features using a running example involving the domain of image manipulation.

Background Surgeons extensively rely on photographic communication for documenting surgical results, teaching and research, and obtaining informed consent from patients. With the advent of digital photography and widespread availability of sophisticated image manipulation software, the potential for committing digital fraud cannot be discounted. Methods Ten ‘before' and ‘after' plastic surgical photographs were selected, and a number of them were digitally enhanced using a standard desktop software by a non-expert in digital photography. A panel of 10 consultant plastic surgeons was asked to judge which, if any of the images had been digitally manipulated. Results Expert assessment had a sensitivity of only 12% in identifying digitally manipulated images. Furthermore, there was poor interobserver agreement with an Intraclass Correlation Coefficient of 0.39. Conclusion Digital fraud is easy to commit and difficult to detect. Furthermore, a number of inadvertent and simple image manipulation functions can also amount to misrepresentation. There may be scope for cooperation within editorial circles to set standards for the submission of digital photographs. Surgeons need also to be aware of the potential for misrepresentation of information through digital image manipulation and exercise caution in the communication of digital photographic information.

OBJECTIVE. This article, the second of two parts, suggests some practical applications for image manipulation using the 2002 version of PowerPoint.CONCLUSION. Applications include enhancing previously manipulated images and manipulating unedited images. The user can develop his or her preferred techniques of image editing.

This study examined the role of cathode-ray tube (CRT) display characterizations on soft-copy image manipulation and perceived color quality of subsequently printed photographs. CRT gamma, color temperature, and excitation purity were manipulated using a central composite experimental design, resulting in fifteen unique monitor calibrations. Twenty-two participants viewed 3 images under each of the 15 CRT conditions. Participants edited color balance, brightness, and contrast of each soft-copy image. The direction and magnitude of image edits were recorded for analysis. Photographic-quality hard-copy prints of each edited soft-copy image also were generated. Participants were brought back to the lab after one month to rate the color quality of the hard-copy prints. Response surface analyses indicated that monitor calibration did influence soft-copy editing and perceived hard-copy color quality of prints generated from the edited images. CRT gamma had the greatest influence on image editing and perceived hard-copy color quality.

Adobe Photoshop image manipulation software is being used for a wide variety of purposes on the furniture and room paneling at the J. Paul Getty Museum. The software was first employed in a project involving the documentation of paneling from five period rooms prior to their conservation. It enabled conservators to document accurately alterations such as restorations and surface damage. The use of the software in the museum has expanded to include the manipulation of digital images to aid their visual interpretation for research purposes; analysis enhancement; accurate comparison of related objects; and as an education tool. The authors outline the materials and equipment most commonly used and conclude with case studies illustrating the range and depth of the possibilities for using image manipulation software in furniture conservation.

Users of complex software applications often learn concepts and skills through step-by-step tutorials. Today, these tutorials are published in two dominant forms: static tutorials composed of images and text that are easy to scan, but cannot effectively describe dynamic interactions; and video tutorials that show all manipulations in detail, but are hard to navigate. We hypothesize that a mixed tutorial with static instructions and per-step videos can combine the benefits of both formats. We describe a comparative study of static, video, and mixed image manipulation tutorials with 12 participants and distill design guidelines for mixed tutorials. We present MixT, a system that automatically generates step-by-step mixed media tutorials from user demonstrations. MixT segments screencapture video into steps using logs of application commands and input events, applies video compositing techniques to focus on salient infor-mation, and highlights interactions through mouse trails. Informal evaluation suggests that automatically generated mixed media tutorials were as effective in helping users complete tasks as tutorials that were created manually.

Part 6: ADVANCED FORENSIC TECHNIQUES; International audience; Splicing is a common image manipulation technique, where parts of multiple images are combined to create a new composite image. Commercial image editing software enables almost anyone to splice images and create fake photographs. This paper investigates how the relationship between object distance and internal depth can aid in detecting spliced stereoscopic images. An equation is derived for predicting the distance at which an object loses internal depth. Experiments with stereoscopic images indicate that the analysis of this depth information can assist in detecting image splicing.

Background: With the adoption of a completely electronic workflow by several journals and the advent of telepathology, digital imaging has become an integral part of every scientific research. However, manipulating digital images is very easy, and it can lead to misinterpretations. Aim: To analyse the impact of manipulating digital images on their diagnosis. Design: Digital images were obtained from Papanicolaou-stained smears of dysplastic and normal oral epithelium. They were manipulated using GNU Image Manipulation Program (GIMP) to alter their brightness and contrast and color levels. A Power Point presentation composed of slides of these manipulated images along with the unaltered originals arranged randomly was created. The presentation was shown to five observers individually who rated the images as normal, mild, moderate or severe dysplasia. Weighted k statistics was used to measure and assess the levels of agreement between observers. Results: Levels of agreement between manipulated images and original images varied greatly among observers. Variation in diagnosis was in the form of overdiagnosis or under-diagnosis, usually by one grade. Conclusion: Global manipulations of digital images of cytological slides can significantly affect their interpretation. Such manipulations should therefore be kept to a minimum, and avoided wherever possible.

This study examined how experienced and less experienced breast screening personnel performed when they examined DICOM screening cases in three conditions: on digital mammography workstations, a LCD monitor and an iPhone In each condition they either viewed the full images unaided or used post-processing manipulations (HCI) For each case they reported features, rated their confidence on abnormality presence and classified the case Their visual search behaviour was recorded as well as behavioural data Additionally, their screening experience was derived from data on a national scheme as well as actual screening information Both experienced and less experienced screeners performed best on the clinical workstation, however good performance was also demonstrated on the monitor using HCI, with iPhone performance being poor Overall, results indicate that low cost devices could be used to provide additional tailored training as long as device resolution and HCI aspects are carefully considered.

The purpose of the research was to identify the factors of the visual identification by means of graphically shaped elements – avatars. In the contemporary times of the online games, communication and visual domination, the virtual identification by means of avatars qualifies as an inevitable element of the visual distinction and identification. Within an online course that was taken by means of Moodle LMS, "Manipulating images with GIMP", the students were given the opportunity of setting up avatars in order to create visual identities.

This article discusses how to use Babanski's theory of the optimization of teaching process to guide the network instructional design in Blended learning. The instructional design and practice of network course "Photoshop Image Manipulation" has been given as an illustrative example, from the three stages of teaching process, that is preparation, conducting, and analysis results phase, teaching process is regarded as a system and studied synthetically. The instructional design process model for blended learning is proposed to promote the optimization of network teaching process, simultaneously enhance the effectiveness of network teaching. It has been proven to receive good instructional evaluations and help us to find ways to comprehensively improve teaching quality.

Amidst many different forms of image manipulation, how to convince a jury of a tampering? Is traditional expert opinions enough? In this paper, using a high-profile Brazilian case as a guideline, we explain how we can take advantage of important statistical methodologies and state-of-the-art techniques to verify evidences of digital tampering, beyond reasonable doubt. (6 pages)

This paper describes a low-cost distributed computing approach for the post-processing of videos. It has been implemented to support the creation of composite videos from various video sources, and supports image manipulation on individual frames of the video. The principle deployment scenario for this work has been to support the automatic creation of output video based on traditional face-to-face lectures. Experimental results suggest that this approach can lead to significant speedups with a reduction in operating costs by leveraging existing underutilized equipment.

A growing number of research misconduct cases handled by the Office of Research Integrity involve image manipulations. Manipulations may include simple image enhancements, misrepresenting an image as something different from what it is, and altering specific features of an image. Through a study of specific cases, the misconduct findings associated with image manipulation, detection methods and those likely to identify such manipulations, are discussed. This article explores sanctions imposed against guilty researchers and the factors that resulted in no misconduct finding although relevant images clearly were flawed. Although new detection tools are available for universities and journals to detect questionable images, this article explores why these tools have not been embraced.