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Lifespan face age transformation aims to generate facial images that accurately depict an individual's appearance at different age stages. This task is highly challenging due to the need for reasonable changes in facial features while preserving identity characteristics. Existing methods tend to synthesize unsatisfactory results, such as entangled facial attributes and low identity preservation, especially when dealing with large age gaps. Furthermore, over‐manipulating the style vector may deviate it from the latent space and damage image quality. To address these issues, this paper introduces a novel nonlinear regression model‐Disentangled Lifespan face Aging (DL‐Aging) to achieve high‐quality age transformation images. Specifically, we propose an age modulation encoder to extract age‐related multi‐scale facial features as key and value, and use the reconstructed style vector of the image as the query. The multi‐head cross‐attention in the W+ space is utilized to update the query for aging image reconstruction iteratively. This nonlinear transformation enables the model to learn a more disentangled mode of transformation, which is crucial for alleviating facial attribute entanglement. Additionally, we introduce a W+ space age regularization term to prevent excessive manipulation of the style vector and ensure it remains within the W+ space during transformation, thereby improving generation quality and aging accuracy. Extensive qualitative and quantitative experiments demonstrate that the proposed DL‐Aging outperforms state‐of‐the‐art methods regarding aging accuracy, image quality, attribute disentanglement, and identity preservation, especially for large age gaps. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) are susceptible to adversarial examples generally generated by adding imperceptible perturbations to the clean images, resulting in the degraded performance of DNNs models. To generate adversarial examples, most methods utilize the L p norm to limit the perturbations and satisfy such imperceptibility. However, the L p norm cannot fully guarantee the semantic authenticity of adversarial examples. Defenses may take advantage of this defect to weaken the attack capability of adversarial examples. Moreover, existing methods with L p restriction have poor generalization ability in white-box attacks and have inferior aggressiveness in black-box attacks. To solve the problems mentioned above, we propose a multiple feature interpolation method to generate face adversarial examples. In the proposed method, we perform the multiple feature interpolation to generate face adversarial examples with new semantics in the process of original image reconstruction and conditional attribute-guided image generation based on StarGAN. Experimental results demonstrate that adversarial examples generated by our method possess new attribute-guided semantics and satisfactory attack success rates under both white-box and black-box settings. [ABSTRACT FROM AUTHOR]

Background: Photon-counting computed tomography (PCCT) enables new ways of image reconstruction, e.g. material decomposition and creation of virtual non-contrast (VNC) series with higher resolution and lower radiation dose than standard computed tomography (CT). Clinical experiences of this are limited. Purpose: To compare true non-contrast (TNC) series with VNC series derived from non-enhanced (VNCu), arterial phase (VNCa) and portal venous phase (VNCv) in clinically approved PCCT. Material and Methods: A total of 45 clinical, tri-phasic abdominal CT scans from the PCCT Naetom Alpha, between February 2022 and November 2022, were retrospectively assessed. Placing a region of interest in six different locations in each VNC series – right liver parenchyma, left liver parenchyma, spleen, aorta, erector spinae muscle, and in the subcutaneous fat – absolute Hounsfield values (HU) and standard deviations (SD) were collected. Differences in HU (ΔHU) were compared and statistically analyzed. Results: Statistically significant differences between VNC and TNC were seen in all measurements, with the largest difference in the subcutaneous fat and the smallest difference in the erector spinae muscle. Only small differences were seen between VNCa and VNCv, where the largest differences were seen in the left and right liver lobes. Conclusion: VNC images from the first-generation clinically approved PCCT showed a significant difference between VNC and TNC images. The differences vary with the type of tissue. Only small differences were seen depending from which contrast phase the VNC was derived. [ABSTRACT FROM AUTHOR]

Shape‐conveying line drawings generated from 3D models normally create closed regions in image space. These lines and regions can be stylized to mimic various artistic styles, but for complex objects, the extracted topology is unnecessarily dense, leading to unappealing and unnatural results under stylization. Prior works typically simplify line drawings without considering the regions between them, and lines and regions are stylized separately, then composited together, resulting in unintended inconsistencies. We present a method for joint simplification of lines and regions simultaneously that penalizes large changes to region structure, while keeping regions closed. This feature enables region stylization that remains consistent with the outline curves and underlying 3D geometry. [ABSTRACT FROM AUTHOR]

The attention of modern media research in the last decade has been focused on the truth value of images: within the field of manipulated photographs, portrait photographs represent a special group, since the human face is a specific visual stimulus that plays an important role in the perception of beauty. The present research investigated portrait photographs that have been automatically retouched by an artificial intelligence-based image editing software, using an online questionnaire. Accordint to the results, viewer’s perception of artificial intelligence based (AI-based) image retouching is not influenced by the gender or age of the photo model, nor by the gender or age of the recipients. However, the beauty judged by the recipients does affect the viewer’s attitude towards both the model and the photograph. The study points to new potential research directions in the field of AI-generated beauty ideals and automated AI-based retouching. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) have revolutionized the field of deep learning by enabling the production of high-quality synthetic data. However, the effectiveness of GANs largely depends on the size and quality of training data. In many real-world applications, collecting large amounts of high-quality training data is time-consuming, and expensive. Accordingly, in recent years, GAN models that use limited data have begun to be developed. In this study, we propose a GAN model that can learn from a single training image. Our model is based on the principle of multiple GANs operating sequentially at different scales, where each GAN learns the features of the training image and transfers them to the next GAN, ultimately generating examples with different realistic structures at the final scale. In our model, we utilized a selfattention and new scaling method to increase the realism and quality of the generated images. The experimental results show that our model performs image generation successfully. In addition, we demonstrated the robustness of our model by testing it in different image manipulation applications. As a result, our model can successfully produce realistic, high-quality, diverse images from a single training image, providing short training time and good training stability. [ABSTRACT FROM AUTHOR]

Visual misinformation poses unique challenges to public health due to its potential for persuasiveness and rapid spread on social media. In this article, librarians at the University of Pittsburgh Health Sciences Library System identify four types of visual health misinformation: misleading graphs and charts, out of context visuals, image manipulation in scientific publications, and AI-generated images and videos. To educate our campus's health sciences audience and wider community on these topics, we have developed a range of instruction about visual health misinformation. We describe our strategies and provide suggestions for implementing visual misinformation programming for a variety of audiences. [ABSTRACT FROM AUTHOR]

3D‐aware generative adversarial networks (GAN) are widely adopted in generating and editing neural radiance fields (NeRF). However, these methods still suffer from GAN‐related issues including degraded diversity and training instability. Moreover, 3D‐aware GANs consider NeRF pipeline as regularizers and do not directly operate with 3D assets, leading to imperfect 3D consistencies. Besides, the independent changes in disentangled editing cannot be ensured due to the sharing of some shallow hidden features in generators. To address these challenges, we propose the first purely diffusion‐based three‐stage framework for generative and editing tasks, with a series of well‐designed loss functions that can directly handle 3D models. In addition, we present a generalizable neural point field as our 3D representation, which explicitly disentangles geometry and appearance in feature spaces. For 3D data conversion, it simplifies the preparation pipeline of datasets. Assisted by the representation, our diffusion model can separately manipulate the shape and appearance in a hierarchical manner by image/text prompts that are provided by the CLIP encoder. Moreover, it can generate new samples by adding a simple generative head. Experiments show that our approach outperforms the SOTA work in the generative tasks of direct generation of 3D representations and novel image synthesis, and completely disentangles the manipulation of shape and appearance with correct semantic correspondence in the editing tasks. [ABSTRACT FROM AUTHOR]

Image rotation makes fake images more realistic in geometric perspective, and the performance of existing JPEG image rotation angle estimation algorithms is easily disturbed by block artifacts and the image block size. It remains an imminent and challenging work for the rotation angle interval [1°,15°]. This study presents an effective algorithm to estimate the rotation angle of JPEG images based on block artifact spectral analysis. Firstly, the edges of the image are extracted and removed using a variant colony algorithm to highlight block effects. Secondly, the effect of image texture is further mitigated by cross-differentiation. Thirdly, extraneous peaks are removed in the Fourier transform domain by setting reasonable thresholds and using a Gaussian high-pass filter to reduce interference. Finally, the amplitude component of the Fourier spectrum is projected into the polar coordinates, and the polar angle corresponding to the peak in the polar coordinates is the estimation of rotation angle we need. The experimental results show that the average absolute error of the method is lower than that of the existing methods for the detection of small-size JPEG images whose rotation angle lies within the [1°,15°] interval. Besides, when the compression quality factor is gradually reduced, the performance of the proposed method is still better than the existing methods, and the robustness of the proposed method to JPEG compression is better. [ABSTRACT FROM AUTHOR]

There has been significant progress in generating an animatable 3D human avatar from a single image. However, recovering texture for the 3D human avatar from a single image has been relatively less addressed. Because the generated 3D human avatar reveals the occluded texture of the given image as it moves, it is critical to synthesize the occluded texture pattern that is unseen from the source image. To generate a plausible texture map for 3D human avatars, the occluded texture pattern needs to be synthesized with respect to the visible texture from the given image. Moreover, the generated texture should align with the surface of the target 3D mesh. In this paper, we propose a texture synthesis method for a 3D human avatar that incorporates geometry information. The proposed method consists of two convolutional networks for the sampling and refining process. The sampler network fills in the occluded regions of the source image and aligns the texture with the surface of the target 3D mesh using the geometry information. The sampled texture is further refined and adjusted by the refiner network. To maintain the clear details in the given image, both sampled and refined texture is blended to produce the final texture map. To effectively guide the sampler network to achieve its goal, we designed a curriculum learning scheme that starts from a simple sampling task and gradually progresses to the task where the alignment needs to be considered. We conducted experiments to show that our method outperforms previous methods qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Intuitively editing the appearance of materials from a single image is a challenging task given the complexity of the interactions between light and matter, and the ambivalence of human perception. This problem has been traditionally addressed by estimating additional factors of the scene like geometry or illumination, thus solving an inverse rendering problem and subduing the final quality of the results to the quality of these estimations. We present a single‐image appearance editing framework that allows us to intuitively modify the material appearance of an object by increasing or decreasing high‐level perceptual attributes describing such appearance (e.g., glossy or metallic). Our framework takes as input an in‐the‐wild image of a single object, where geometry, material, and illumination are not controlled, and inverse rendering is not required. We rely on generative models and devise a novel architecture with Selective Transfer Unit (STU) cells that allow to preserve the high‐frequency details from the input image in the edited one. To train our framework we leverage a dataset with pairs of synthetic images rendered with physically‐based algorithms, and the corresponding crowd‐sourced ratings of high‐level perceptual attributes. We show that our material editing framework outperforms the state of the art, and showcase its applicability on synthetic images, in‐the‐wild real‐world photographs, and video sequences. [ABSTRACT FROM AUTHOR]

Signage systems play an essential role in ensuring safe, stress‐free, and efficient navigation for the occupants of indoor spaces. Crowd simulations with sufficiently realistic virtual humans provide a convenient and cost‐effective approach to evaluating and optimizing signage systems. In this work, we develop an agent model which makes use of image processing on parametric saliency maps to visually identify signage and distractions in the agent's field of view. Information from identified signs is incorporated into a grid‐based representation of wayfinding familiarity, which is used to guide informed exploration of the agent's environment using a modified A* algorithm. In areas with low wayfinding familiarity, the agent follows a random exploration behaviour based on sampling a grid of previously observed locations for heuristic values based on space syntax isovist measures. The resulting agent design is evaluated in a variety of test environments and found to be able to reliably navigate towards a goal location using a combination of signage and random exploration. [ABSTRACT FROM AUTHOR]

The ability to edit an image using intuitive commands and primitives is a desired feature for any image editing software. In this paper, we combine recent results in medial axes with the well-established morphological tree representations to develop an interactive image editing tool that provides global and local image manipulation using high-level primitives. We propose a new way to render interactive morphological trees using icicle plots and introduce different ways of manipulating spline-based medial axis transforms for grayscale and colored image editing. Different applications of the tool, such as watermark removal, image deformation, dataset augmentation for machine learning, artistic illumination manipulation, image rearrangement, and clothing design, are described and showcased on examples. [Display omitted] • A tool using spline-based medial axis transforms for image editing. • A new way to render interactive morphological trees using icicle plots. • Watermark removal and image deformation applications. • Dataset augmentation and image rearrangement applications. [ABSTRACT FROM AUTHOR]

Plasmonic nanomaterials have revolutionized sensing and imaging technologies due to their unique optical properties, particularly surface plasmon resonance (SPR). These materials offer enhanced sensitivity and resolution, making them promising candidates for applications in deepfake image detection, where accurate authentication of digital content is crucial. This work presents the application of plasmonic nanomaterials in enhancing sensing and imaging capabilities for deepfake detection. Gold nanoparticles functionalized with specific ligands are employed to exploit SPR effects, enabling sensitive detection of minute alterations in image content. A spectroscopic setup is utilized to measure the SPR shifts corresponding to changes induced by deepfake manipulations. Experimental results demonstrate that the SPR-based sensing approach achieves a detection accuracy of over 95% in distinguishing deepfake images from authentic ones. The SPR sensor exhibits a high signal-to-noise ratio, providing robust performance even in complex imaging scenarios with varying lighting conditions and image resolutions. Plasmonic nanomaterials, leveraging SPR, offer a reliable method for enhancing deepfake image detection capabilities. The demonstrated high accuracy and sensitivity underscore their potential in combating digital media forgery, contributing to the development of more secure and trustworthy authentication systems for visual content. [ABSTRACT FROM AUTHOR]

3D‐aware generative models have demonstrated their superb performance to generate 3D neural radiance fields (NeRF) from a collection of monocular 2D images even for topology‐varying object categories. However, these methods still lack the capability to separately control the shape and appearance of the objects in the generated radiance fields. In this paper, we propose a generative model for synthesizing radiance fields of topology‐varying objects with disentangled shape and appearance variations. Our method generates deformable radiance fields, which builds the dense correspondence between the density fields of the objects and encodes their appearances in a shared template field. Our disentanglement is achieved in an unsupervised manner without introducing extra labels to previous 3D‐aware GAN training. We also develop an effective image inversion scheme for reconstructing the radiance field of an object in a real monocular image and manipulating its shape and appearance. Experiments show that our method can successfully learn the generative model from unstructured monocular images and well disentangle the shape and appearance for objects (e.g., chairs) with large topological variance. The model trained on synthetic data can faithfully reconstruct the real object in a given single image and achieve high‐quality texture and shape editing results. [ABSTRACT FROM AUTHOR]

Latent space exploration is a technique that discovers interpretable latent directions and manipulates latent codes to edit various attributes in images generated by generative adversarial networks (GANs). However, in previous work, spatial control is limited to simple transformations (e.g., translation and rotation), and it is laborious to identify appropriate latent directions and adjust their parameters. In this paper, we tackle the problem of editing the StyleGAN image layout by annotating the image directly. To do so, we propose an interactive framework for manipulating latent codes in accordance with the user inputs. In our framework, the user annotates a StyleGAN image with locations they want to move or not and specifies a movement direction by mouse dragging. From these user inputs and initial latent codes, our latent transformer based on a transformer encoder‐decoder architecture estimates the output latent codes, which are fed to the StyleGAN generator to obtain a result image. To train our latent transformer, we utilize synthetic data and pseudo‐user inputs generated by off‐the‐shelf StyleGAN and optical flow models, without manual supervision. Quantitative and qualitative evaluations demonstrate the effectiveness of our method over existing methods. [ABSTRACT FROM AUTHOR]

High‐quality portrait image editing has been made easier by recent advances in GANs (e.g., StyleGAN) and GAN inversion methods that project images onto a pre‐trained GAN's latent space. However, extending the existing image editing methods, it is hard to edit videos to produce temporally coherent and natural‐looking videos. We find challenges in reproducing diverse video frames and preserving the natural motion after editing. In this work, we propose solutions for these challenges. First, we propose a video adaptation method that enables the generator to reconstruct the original input identity, unusual poses, and expressions in the video. Second, we propose an expression dynamics optimization that tweaks the latent codes to maintain the meaningful motion in the original video. Based on these methods, we build a StyleGAN‐based high‐quality portrait video editing system that can edit videos in the wild in a temporally coherent way at up to 4K resolution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The field of scientific image integrity presents a challenging research bottleneck given the lack of available datasets to design and evaluate forensic techniques. The sensitivity of data also creates a legal hurdle that restricts the use of real-world cases to build any accessible forensic benchmark. In light of this, there is no comprehensive understanding on the limitations and capabilities of automatic image analysis tools for scientific images, which might create a false sense of data integrity. To mitigate this issue, we present an extendable open-source algorithm library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. We create a large scientific forgery image benchmark (39,423 images) with enriched ground truth using this library and realistic scientific images. All figures within the benchmark are synthetically doctored using images collected from creative commons sources. While collecting the source images, we ensured that the they did not present any suspicious integrity problems. Because of the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. All evaluated methods had a low performance in this dataset, indicating that scientific images might need a specialized copy-move detector. The dataset and source code are available at . [ABSTRACT FROM AUTHOR]

Background: Safe and accurate execution of surgeries to date mainly rely on preoperative plans generated based on preoperative imaging. Frequent intraoperative interaction with such patient images during the intervention is needed, which is currently a cumbersome process given that such images are generally displayed on peripheral two-dimensional (2D) monitors and controlled through interface devices that are outside the sterile filed. This study proposes a new medical image control concept based on a Brain Computer Interface (BCI) that allows for hands-free and direct image manipulation without relying on gesture recognition methods or voice commands.Method: A software environment was designed for displaying three-dimensional (3D) patient images onto external monitors, with the functionality of hands-free image manipulation based on the user's brain signals detected by the BCI device (i.e., visually evoked signals). In a user study, ten orthopedic surgeons completed a series of standardized image manipulation tasks to navigate and locate predefined 3D points in a Computer Tomography (CT) image using the developed interface. Accuracy was assessed as the mean error between the predefined locations (ground truth) and the navigated locations by the surgeons. All surgeons rated the performance and potential intraoperative usability in a standardized survey using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree).Results: When using the developed interface, the mean image control error was 15.51 mm (SD: 9.57). The user's acceptance was rated with a Likert score of 4.07 (SD: 0.96) while the overall impressions of the interface was rated as 3.77 (SD: 1.02) by the users. We observed a significant correlation between the users' overall impression and the calibration score they achieved.Conclusions: The use of the developed BCI, that allowed for a purely brain-guided medical image control, yielded promising results, and showed its potential for future intraoperative applications. The major limitation to overcome was noted as the interaction delay. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. This state‐of‐the‐art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. Among StyleGAN's most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well‐behaved and remarkably disentangled. Combined with StyleGAN's visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator's learned distribution, and can only be applied to images generated by StyleGAN itself. Seeking to bring StyleGAN's latent control to real‐world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. We map out StyleGAN's impressive story through these investigations, and discuss the details that have made StyleGAN the go‐to generator. We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks. Looking forward, we point out StyleGAN's limitations and speculate on current trends and promising directions for future research, such as task and target specific fine‐tuning. [ABSTRACT FROM AUTHOR]

We present ZoomShop, a photographic composition editing tool for adjusting relative size, position, and foreshortening of scene elements. Given an image and corresponding depth map as input, ZoomShop combines a novel non‐linear camera model and a depth‐aware image warp to reproject and deform the image. Users can isolate objects by selecting depth ranges and adjust their scale and foreshortening, which controls the paths of the camera rays through the scene. Users can also select 2D image regions and translate them, which determines the objective function in the image warp optimization. We demonstrate that ZoomShop can be used to achieve useful compositional goals, such as making a distant object more prominent while preserving foreground scenery, or making objects both larger and closer together so they still fit in the frame. [ABSTRACT FROM AUTHOR]

Image manipulation with text is to semantically modify the appearance of an object in a source image based on the given text describing the novel visual attributes while retaining other irrelevant information in the image, such as the background. This has a wide range of applications, such as intelligent image manipulation, and is helpful to those who are not good at painting. We propose a generative adversarial network having a pair of discriminators with different architectures, namely Paired-D++ GAN, for image manipulation with text where the two discriminators make different judgments: one for foreground synthesis and the other for background synthesis. The generator of Paired-D++ GAN has the encoder–decoder architecture with skip-connections and synthesizes an object’s appearance matching the given text description while preserving other parts of the source image. The two discriminators judge the foreground and background of the synthesized image separately to meet the given input text description and the given source image. The Paired-D++ GAN is trained using the effectively unconditional and conditional adversarial learning process in a simultaneous three-player minimax game. Our comprehensively experimental results on the Caltech-200 bird dataset and the Oxford-102 flower dataset show that Paired-D++ GAN can semantically synthesize images to match an input text description while retaining the background in a source image against the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: Digital 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/approach: Written 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. Findings: There 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/value: Information 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. [ABSTRACT FROM AUTHOR]

Recent successes suggest that an image can be manipulated by a text prompt, e.g., a landscape scene on a sunny day is manipulated into the same scene on a rainy day driven by a text input "raining". These approaches often utilize a StyleCLIP-based image generator, which leverages multi-modal (text and image) embedding space. However, we observe that such text inputs are often bottlenecked in providing and synthesizing rich semantic cues, e.g., differentiating heavy rain from rain with thunderstorms. To address this issue, we advocate leveraging an additional modality, sound, which has notable advantages in image manipulation as it can convey more diverse semantic cues (vivid emotions or dynamic expressions of the natural world) than texts. In this paper, we propose a novel approach that first extends the image–text joint embedding space with sound and applies a direct latent optimization method to manipulate a given image based on audio input, e.g., the sound of rain. Our extensive experiments show that our sound-guided image manipulation approach produces semantically and visually more plausible manipulation results than the state-of-the-art text and sound-guided image manipulation methods, which are further confirmed by our human evaluations. Our downstream task evaluations also show that our learned image–text-sound joint embedding space effectively encodes sound inputs. Examples are provided in our project page: https://kuai-lab.github.io/robust-demo/. • We propose a novel method for robust sound-guided image manipulation. • We demonstrate that our learned embedding space is effective by extensive comparative experiments. • We show an in-depth analysis of our sound-guided image manipulation method. [ABSTRACT FROM AUTHOR]

Text-guided image editing (TIE) seeks to manipulate images by the guidance of language. However, the existing TIE methods always overlook the target-irrelevant pixels and the editing may make the background discolored, distorted, or partially disappear. To overcome this problem, we propose a novel TIE method named FocusGAN, which edits the text-relevant pixels precisely as well as keeps the background invariant. Specifically, we build a network of two stages. In each stage, we first construct a channel-wise subject focusing attention to make the generator focus on the sub-region that best matches each word. Then, the word-level discriminator provides fine-grained feedback by correlating words with image areas, so that the generator can manipulate specific visual attributes without affecting the background. Last, we propose a background-keeping cyclic loss to further improve the invariance of the background and to encourage the edit of the subject that matches the given text. Experiments on CUB and Oxford datasets demonstrate that our approach can effectively keep the background invariant in manipulating images using natural language descriptions. [ABSTRACT FROM AUTHOR]

There is a long history of tourists substantiating their visits to a destination through the purchase of portraits that show them against a backdrop of the local setting. While its initial expression in the form of paintings was confined to the social elite who could afford to commission and sit for an artist, the advent of photography democratized the process, enabling the aspiring middle classes to partake in the custom. While some tourists took their own photographs, the majority relied on local photographers who offered their services in studio and open-air settings. Smaller-sized images, such as Cartes de Visite (2.5″ × 4″) and Cabinet Cards (4.5″ × 6.5″), could be enclosed with letters to family and social circles, thus providing proof of visits while the voyage was still in progress. The development of picture postcards as a postal item in the 1890s, coupled with the manufacture of precut photographic paper with preprinted address fields, revolutionized tourist portraiture. Photographers could set-up outside tourist attractions, where tourists could have their portrait taken with formulaic framing against a canonized background. Efficient production flows meant that tourists could pick up their printed portraits, ready for mailing within an hour. Using examples of San Marc's Basilica in Venice (Italy), as well as Ostrich Farms in California and Florida (U.S.A.), this paper contextualizes the production and consumption of such commercial tourist portraits as objects of social validation. It discusses their ability to situate the visitor in locales iconic of the destination, substantiating their presence and validating their experience. Given the speed of production (within an hour) and their ability to be immediately mailed through the global postal network, such images were the precursor of the modern-day 'selfies' posted on social media. [ABSTRACT FROM AUTHOR]

A postcard 'craze' engulfed the developed world and colonial world during the end of the nineteenth and the beginning of the twentieth century, when over 2.3 million picture postcards were mailed in 1904 alone. Formally published picture postcards can provide a rich source of information for heritage studies as they depict landscape scenery, towns, individual buildings and public plantings such as parks. The evidentiary value of late nineteenth and early twentieth century postcards depends on the veracity of the depicted image. While based on photographs, processes of postcard production allowed the publisher to modify the original imagery to improve the messaging entailed in the image. Modes of image manipulation, such as retouching, can sufficiently alter the content of the image to create limitations to using published postcard imagery as a tool for historic landscape and building analysis. This is the first paper to systematically discuss the process of postcard production and the manipulation of images depicted on the view size of picture postcards. It demonstrates that where evidentiary emphasis is placed on postcard images, it is imperative that a systematic search for variants is carried out. [ABSTRACT FROM AUTHOR]

Scientific experiments rely on some type of measurements that provide the required data to extract aimed information or conclusions. Data production and analysis are, therefore, essential components at the heart of any scientific experimental application. Traditionally, efforts on detector development for photon sources have focused on the properties and performance of the detection front-ends. In many cases, the data acquisition chain as well as data processing are treated as a complementary component of the detector system and added at a later stage of the project. In most cases, data processing tasks are entrusted to CPUs; achieving, thus, the minimum bandwidth requirements and keeping hardware relatively simple in terms of functionalities. This also minimizes design effort, complexity, and implementation cost. This approach is changing in the last few years as it does not accommodate new high-performance detectors; field-programmable gate array (FPGA) and GPUs are now used to perform complex image manipulation tasks such as image reconstruction, image rotation, accumulation, filtering, and data analysis. This frees up CPUs for simpler tasks. The objective of this article is to present both the implementation of real-time FPGA-based image manipulation techniques, as well as the performance of the European Synchrotron radiation Facility (ESRF) data acquisition platform called remote direct memory access (RDMA)-based acquisition system for high-performance applications (RASHPA), into the back-end board of the SMARTPIX photon-counting detector developed at the ESRF. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Transferring human motion and appearance between videos of human actors remains one of the key challenges in Computer Vision. Despite the advances from recent image-to-image translation approaches, there are several transferring contexts where most end-to-end learning-based retargeting methods still perform poorly. Transferring human appearance from one actor to another is only ensured when a strict setup has been complied, which is generally built considering their training regime's specificities. In this work, we propose a shape-aware approach based on a hybrid image-based rendering technique that exhibits competitive visual retargeting quality compared to state-of-the-art neural rendering approaches. The formulation leverages the user body shape into the retargeting while considering physical constraints of the motion in 3D and the 2D image domain. We also present a new video retargeting benchmark dataset composed of different videos with annotated human motions to evaluate the task of synthesizing people's videos, which can be used as a common base to improve tracking the progress in the field. The dataset and its evaluation protocols are designed to evaluate retargeting methods in more general and challenging conditions. Our method is validated in several experiments, comprising publicly available videos of actors with different shapes, motion types, and camera setups. The dataset and retargeting code are publicly available to the community at: https://www.verlab.dcc.ufmg.br/retargeting-motion. [ABSTRACT FROM AUTHOR]

Despite the great success of Generative Adversarial Networks (GANs) in synthesizing images, there lacks enough understanding of how photo-realistic images are generated from the layer-wise stochastic latent codes introduced in recent GANs. In this work, we show that highly-structured semantic hierarchy emerges in the deep generative representations from the state-of-the-art GANs like StyleGAN and BigGAN, trained for scene synthesis. By probing the per-layer representation with a broad set of semantics at different abstraction levels, we manage to quantify the causality between the layer-wise activations and the semantics occurring in the output image. Such a quantification identifies the human-understandable variation factors that can be further used to steer the generation process, such as changing the lighting condition and varying the viewpoint of the scene. Extensive qualitative and quantitative results suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize various concepts in a hierarchical manner: the early layers tend to determine the spatial layout, the middle layers control the categorical objects, and the later layers render the scene attributes as well as the color scheme. Identifying such a set of steerable variation factors facilitates high-fidelity scene editing based on well-learned GAN models without any retraining (code and demo video are available at https://genforce.github.io/higan). [ABSTRACT FROM AUTHOR]

Altering the content of an image with photo editing tools is a tedious task for an inexperienced user, especially, when modifying the visual attributes of a specific object in an image without affecting other constituents such as background etc. To simplify the process of image manipulation and to provide more control to users, it is better to utilize a simpler interface like natural language. It also enables to semantically modify parts of an image according to the given text. Therefore, in this paper, we address the challenge of manipulating images using natural language descriptions. We propose the Two-sidEd Attentive conditional Generative Adversarial Network (TEA-cGAN) to generate semantically manipulated images. TEA-cGAN's contribution is seen as two-fold. The first contribution aims to attend locations that need to be modified during generation. It introduces two types of architectures that provide fine-grained attention both in the generator and discriminator of Generative Adversarial Network (GAN). To be specific, the first one i.e., the Single-scale architecture used in the generator focuses to modify only the text-relevant regions in an image and leaves other regions untouched. While the second one i.e., Multi-scale architecture further extended this idea by taking the different scales of image features into account. The second contribution purpose is to generate higher resolution images (e.g., 256 × 256) as they provide better quality and stability. Quantitative and qualitative experiments conducted on CUB and Oxford-102 datasets confirm that TEA-cGAN different scale architectures outperform existing methods while generating 128 × 128 resolution images including generating higher resolution image i.e., 256 × 256. [ABSTRACT FROM AUTHOR]

Semantic image synthesis is a process for generating photorealistic images from a single semantic mask. To enrich the diversity of multimodal image synthesis, previous methods have controlled the global appearance of an output image by learning a single latent space. However, a single latent code is often insufficient for capturing various object styles because object appearance depends on multiple factors. To handle individual factors that determine object styles, we propose a class‐ and layer‐wise extension to the variational autoencoder (VAE) framework that allows flexible control over each object class at the local to global levels by learning multiple latent spaces. Furthermore, we demonstrate that our method generates images that are both plausible and more diverse compared to state‐of‐the‐art methods via extensive experiments with real and synthetic datasets in three different domains. We also show that our method enables a wide range of applications in image synthesis and editing tasks. [ABSTRACT FROM AUTHOR]

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 learning or artificial intelligence to map an existing image, usually a photo of a person's face, to transfer that image to an existing video image. The technology emerged into the latter part of 2017, and has since given rise to apps and other programmes that allow users to create their own deepfakes. We already use filters and emojis to alter images by consent, however, deepfakes are particularly problematic because they allow for production of videos that are highly convincing, taken to be a real video of the person depicted. Deepfakes provide for the manipulation of all manner of video, but particular risks include videos produced to incite political deception, voter manipulation, commercial fraud, and 'revenge porn'. The production of deepfake 'revenge porn' presents as especially insidious given the ability to transfer the face of any person onto an already existing pornographic video. Harm is exacerbated where that video is then disseminated, via the internet or by social media. [ABSTRACT FROM AUTHOR]

Manipulating digital images is not often a difficult task due to the rapid development of software and image manipulation techniques. Hence, there is no need for professional skills or training. When used as an artistic tool, it is completely harmless, but when these images can be presented in judicial system as evidence or for the creation of political associations, as well as, using them in legal documents, electronic money circulation or press; in these cases, the distinction between an original image and a forgery image is very important. In order to solve the problem in this research using a Discrete Wavelet Transform (DWT), which is performed by decomposing a signal into smaller and smaller details, as well as, the use of periodic patterns in the histogram generated by double compression with different coefficients, significant improvements were made in terms of reducing computations and increasing the detection rate of forging areas. Many of the proposed methods for detecting image forgery use a feature extraction model from a valid and manipulated dataset and then classify them using machine learning with the aim of optimizing the accuracy. In this research, following the extraction of features in the proposed method, using the SVM classification identifies image forgery and then identifies the forging area after it detects the falsification or originality of the image. The results of this study indicate 97.98% accuracy in the Columbia database and 98.1% in the IFS-TC database. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• An image fusion algorithm, called AHIEA_LMSE algorithm, was proposed first, from which 16-bit high-gray images were generated. • The AHIEA_LMSE algorithm was applied successfully in the field of not only enhancement for infrared image, SAR image, X-ray film image, but also infrared target detection. • Compared with the other traditional enhancement algorithms, all the indexes of images, enhanced by AHIEA_LMSE algorithm, have been improved. In the fields of industry, security, military and other areas, high gray-level low contrast images have been widely used. However, due to the large dynamic range of gray level, high noise, blurry detail information and low contrast, it brings difficulties to subsequent image enhancement, target detection and other processes. Ordinary image enhancement algorithms are only suitable for conventional 8-bit images, which are not effective to deal with high-gray and low-contrast images. To address these issues, this paper proposed an adaptive high-gray image enhancement algorithm based on logarithmic mapping and simulated exposure, which was used for processing X-ray film images, infrared images, SAR images and other high gray-level low contrast images. Firstly, a detailed algorithm model construction method was provided for high gray-level low contrast images. Secondly, in order to verify the universality of the algorithm proposed in this paper, qualitative and quantitative experimental analyses were carried out using X-ray film images, infrared images and SAR images respectively. Finally, taking the most complex infrared image in the application environment as an example, the images, enhanced by the algorithm proposed in this paper, were sent to the YOLOv8 neural network for target detection, which improved the detection accuracy. The experiment shows that the algorithm, proposed in this paper, can significantly improve the image quality of high gray-level low contrast images, the detail information is obvious and the contrast is significantly improved. Various indicators are far higher than those of traditional algorithms. At the same time, this algorithm can provide reliable technical support for applications, such as target detection and target tracking in this field. [ABSTRACT FROM AUTHOR]

Vectorizing line drawing is necessary for the digital workflows of 2D animation and engineering design. But it is challenging due to the ambiguity of topology, especially at junctions. Existing vectorization methods either suffer from low accuracy or cannot deal with high‐resolution images. To deal with a variety of challenging containing different kinds of complex junctions, we propose a two‐phase line drawing vectorization method that analyzes the global and local topology. In the first phase, we subdivide the lines into partial curves, and in the second phase, we reconstruct the topology at junctions. With the overall topology estimated in the two phases, we can trace and vectorize the curves. To qualitatively and quantitatively evaluate our method and compare it with the existing methods, we conduct extensive experiments on not only existing datasets but also our newly synthesized dataset which contains different types of complex and ambiguous junctions. Experimental statistics show that our method greatly outperforms existing methods in terms of computational speed and achieves visually better topology reconstruction accuracy. [ABSTRACT FROM AUTHOR]

• Proposing MANFA – a customized CNN model for manipulated face detection. • Integrating XGBoost, and AdaBoost with MANFA to cope with the extreme imbalanced dataset. • Proposing a manually collected dataset (8950 images) for altered face detection. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Computer generated images are assumed to be a key part in each person's life in this era of information technology, where individuals effectively inhabit the advertisements, magazines, websites, televisions and many more. At the point when digital images played their role, the event of violations in terms of misrepresentation of information, use of their wrong doings winds up and also becomes easier with the help of image editing application programs. To be legitimate, if anyone does wrong anything then the proposed method can be used for a correct identification of the forgery and the imitations in the digital images. In existing techniques, researchers have suggested most well-known types of digital photographic manipulations based on source, meta-data, image copying, splicing and many more. The proposed approach is inspired by physics-based techniques and requires less human involvement. The presented approach works for images having any type of objects present in the scene, i.e. not only limited to human faces and selection of same intensity regions of the image. By assessing the lighting parameters, the proposed technique identifies the manipulated object and returns angle of incidence w.r.t light source direction. The demonstrated result produces forgery recognition rate of 92% on an image dataset comprising of various types of manipulated images. [ABSTRACT FROM AUTHOR]

With the phenomenal development in processing power and the dropping cost of high-resolution imaging devices and computer hardware, coupled with the availability of sophisticated user-friendly image manipulation programs, the instances of forgery in digital images has increased manifold. In the current scenario, Digital Image Forensics is an area that has gained immense importance and is instrumental for detecting image forgery. Digital image forgery detection includes Blind or Passive techniques that don't require any previous information of an image, as the tampered image carries inconsistencies that can be used to detect forgery. Imaging devices and processing techniques, however different from each other, carry a consistent pattern in the image which, if tampered would introduce a deviation from the original pattern. This deviation enables one to detect forgery of images. In this paper, various passive forgery detection techniques are discussed with a special focus on pixel- and physics-based techniques. This paper aims is to deliver an overview of emerging techniques in the field of Image Forgery Detection. [ABSTRACT FROM AUTHOR]

Fake or manipulated images propagated through the Web and social media have the capacity to deceive, emotionally distress, and influence public opinions and actions. Yet few studies have examined how individuals evaluate the authenticity of images that accompany online stories. This article details a 6-batch large-scale online experiment using Amazon Mechanical Turk that probes how people evaluate image credibility across online platforms. In each batch, participants were randomly assigned to 1 of 28 news-source mockups featuring a forged image, and they evaluated the credibility of the images based on several features. We found that participants' Internet skills, photo-editing experience, and social media use were significant predictors of image credibility evaluation, while most social and heuristic cues of online credibility (e.g. source trustworthiness, bandwagon, intermediary trustworthiness) had no significant impact. Viewers' attitude toward a depicted issue also positively influenced their credibility evaluation. [ABSTRACT FROM AUTHOR]

Purpose: To explore clinical applications of a novel conventional computed tomography (CT) color post-processing algorithm to increase conspicuity of nondisplaced trabecular fractures.Materials and Methods: The algorithm was created in Adobe Photoshop and Adobe Extendscript, utilizing DICOM images from conventional CT as source images. A total of six representative cases were selected and processed. No statistical analyses were performed.Results: A total of six cases are demonstrated, five with MRI correlation demonstrating corresponding fractures and bone marrow edema, including a case of sacral insufficiency fracture, two cases of vertebral body fracture, two cases of nondisplaced hip fracture, and a knee bone marrow edema lesion (without MRI correlate). All cases were processed successfully without error.Conclusions: A conventional CT color post-processing algorithm may be clinically useful in increasing conspicuity of nondisplaced fractures and bone marrow edema. A potential pitfall is the presence of subchondral or marrow sclerosis, which may mimic edema. Future prospective studies will be necessary to evaluate diagnostic performance. [ABSTRACT FROM AUTHOR]

Abstract: Reshaping faces is an interesting thing and a common demand in today’s media industry and cosmetic industry. Especially, the reshaped face images contribute a lot to special effects industry and plastic-beauty industry. In this paper, we provide a novel parametric representation of 3D face model that describes the face shape as a linear combination of semantic and non-semantic bases. The semantic (local) bases correspond to each individual face part and they are used to semantically edit each face part, while the non-semantic bases explain other shape variations to span the subspace of the face shape. First, we build a sparse and spatially localized parametric face model from a dataset of 3D face models by sparse principal component analysis. Second, to define the semantic bases, we train a regression model to correlate semantically significant values like nose height and mouth width. Finally, the bases of the resulting parametric face model is orthogonalized to all defined semantic bases by the Gram-Schmidt algorithm for generating the novel parametric representation of 3D face model. The novel representation can be applied in reshaping face in the images. The experiment results demonstrate that our representation not only retains the accuracy for 3D face reconstruction but also provides users a user-friendly tool to edit facial parts for desired facial shapes.Graphical abstract: [ABSTRACT FROM AUTHOR]

We propose a fully automatic approach to restore aged old line drawings. We decompose the task into two subtasks: the line extraction subtask, which aims to extract line fragments and remove the paper texture background, and the restoration subtask, which fills in possible gaps and deterioration of the lines to produce a clean line drawing. Our approach is based on a convolutional neural network that consists of two sub-networks corresponding to the two subtasks. They are trained as part of a single framework in an end-to-end fashion. We also introduce a new dataset consisting of manually annotated sketches by Leonardo da Vinci which, in combination with a synthetic data generation approach, allows training the network to restore deteriorated line drawings. We evaluate our method on challenging 500-year-old sketches and compare with existing approaches with a user study, in which it is found that our approach is preferred 72.7% of the time. [ABSTRACT FROM AUTHOR]

With the advancement of image editing tools in today's world, the manipulation of images like cropping, cloning, resizing, etc., becomes an easy proposition and on the other end, checking or determining whether an image has been manipulated or not, becomes a great challenge. Copy‐move forgery in images is the most popular tampering method in which a portion of an image is copied and pasted in some other location of the same image. The detection of copy‐move forgery has become a prominent research area. This study presents a detailed review and critical discussions with pros and cons of each of copy‐move forgery detection techniques from 2007 to 2017. This study also addresses the variation in databases, issues, challenges, future directions and references in this domain. [ABSTRACT FROM AUTHOR]

Previous research has explored how the content of landscape photography (i.e„ natural vs. human-made) and postmanipulation of photographs (e.g., clarity and color) can influence aesthetic judgments. Although natural landscapes are reliably rated as more likable compared with human-made landscapes, very little is known about combined natural and human-made landscapes that depict alterations of the natural world by human interventions. After categorizing the works of Edward Burtynsky as "combined" landscapes along a continuum between natural and human-made landscape photographs, participants rated the likability and familiarity (i.e., whether landscapes were previously viewed or not) of all three types of images in a series of three experiments that measured likability and familiarity differences as a function of landscape type (Experiment 1), postmanipulation of color (Experiment 2), and postmanipulation of image clarity (Experiment 3). Natural photographs were rated significantly higher than all other photograph types (regardless of color or clarity manipulation), and combined photographs were significantly rated the lowest in all experimental conditions, especially those that were previously viewed. Across all conditions, previously viewed photographs were reliably discriminated from those that were not. The results suggest that the combined Burtynsky photographs fall outside a continuum of likability between natural and human-made extremes, and such a low aesthetic rating of previously viewed combined photographs may be because of negative social priming, an altered fluency processing, or both. [ABSTRACT FROM AUTHOR]

This article deals with the representation of reality in documentary and describes a new category of documentary, namely, the objectifying documentary. The objectifying documentary originated in the 2000s, with examples such as Bosnia. Lost Images (2003), and questions a certain type of war propaganda. It pursues two goals: to deconstruct the conventions of the realist style and reveal the artifice behind its supposed immediacy and claim to provide “evidence" of what happened; and to tell a story from a rational, “objectified" point of view. It therefore takes on the appearance of an epistemological tool informed by scientific rationality. In this paper I first describe the characteristics of the realist style in documentary, which was hegemonic until the end of the twentieth century. I then develop the concept of the objectifying documentary and describe its visual and narrative strategies. My methodology is that of aesthetic analysis, as proposed by Corner (2003). I illustrate the concept of the objectifying documentary through the case study of a realist-style news report on the killing of Muhammad Al-Durrah in 2000, which has since been objectified by the documentary Das Kind, der Tod und die Wahrheit (2009). In the last section I analyse one of the most powerful visual strategies for constructing documentary objectivity, namely, the manipulation of temporality. In the conclusions I reflect on the presumed rationality and scientific nature of objectifying documentaries and their epistemic ideology. [ABSTRACT FROM AUTHOR]