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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]

Synthetic images ar---e created using computer graphics modeling and artificial intelligence techniques, referred to as deepfakes. They modify human features by using generative models and deep learning algorithms, posing risks violations of social media regulations and spread false information. To address these concerns, the study proposed an improved generative adversarial network (GAN) model which improves accuracy while differentiating between real and fake images focusing on data augmentation and label smoothing strategies for GAN training. The study utilizes a dataset containing human faces and employs DCGAN (deep convolutional generative adversarial network) as the base model. In comparison with the traditional GANs, the proposed GAN outperform in terms of frequently used metrics i.e., Fréchet Inception Distance (FID) and accuracy. The model effectiveness is demonstrated through evaluation on the Flickr-Faces Nvidia dataset and Fakefaces d--ataset, achieving an FID score of 55.67, an accuracy of 98.82%, and an F1-score of 0.99 in detection. This study optimizes the model parameters to achieve optimal parameter settings. This study fine-tune the model parameters to reach optimal settings, thereby reducing risks in synthetic image generation. The article introduces an effective framework for both image manipulation and detection. [ABSTRACT FROM AUTHOR]

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

In the contemporary digital era, images are omnipresent, serving as pivotal entities in conveying information, authenticating experiences, and substantiating facts. The ubiquity of image editing tools has precipitated a surge in image forgeries, notably through copy-move attacks where a portion of an image is copied and pasted within the same image to concoct deceptive narratives. This phenomenon is particularly perturbing considering the pivotal role images play in legal, journalistic, and scientific domains, necessitating robust forgery detection mechanisms to uphold image integrity and veracity. While advancements in Convolutional Neural Networks (CNN) have propelled copy-move forgery detection, existing methodologies grapple with limitations concerning the detection efficacy amidst complex manipulations and varied dataset characteristics. Additionally, a palpable void exists in comprehensively understanding and exploiting dataset heterogeneity to enhance detection capabilities. This heralds a pronounced exigency for innovative CNN architectures and nuanced understandings of dataset intricacies to augment detection capabilities, which has remained notably underexplored in the prevailing literature. Against this backdrop, our research broaches novel frontiers in copy-move forgery detection by introducing an innovative CNN architecture meticulously tailored to discern the subtlest manipulations, even amidst intricate image contexts. An extensive analysis of multiple datasets – MICC-F220, MICC-F600, and a combined variant – enables us to delineate a granular understanding of their attributes, thereby shedding unprecedented light on their influences on detection performance. Further, our research goes beyond mere detection, delving deep into comprehensive analyses of varied datasets and conducting additional experiments with differential training-validation sets and randomly labeled data to scrutinize the robustness and reliability of our model. We not only meticulously document and analyze our findings but also juxtapose them against extant models, offering an exhaustive comparative analysis. [ABSTRACT FROM AUTHOR]

Splice detection models are the need of the hour since splice manipulations can be used to mislead, spread rumors and create disharmony in society. However, there is a severe lack of image-splicing datasets, which restricts the capabilities of deep learning models to extract discriminative features without overfitting. This manuscript presents twofold contributions toward splice detection. Firstly, a novel splice detection dataset is proposed having two variants. The two variants include spliced samples generated from code and through manual editing. Spliced images in both variants have corresponding binary masks to aid localization approaches. Secondly, a novel spatio-compression lightweight splice detection framework is proposed for accurate splice detection with minimum computational cost. The proposed dual-branch framework extracts discriminative spatial features from a lightweight spatial branch. It uses original resolution compression data to extract double compression artifacts from the second branch, thereby making it 'information preserving.' Several CNNs are tested in combination with the proposed framework on a composite dataset of images from the proposed dataset and the CASIA v2.0 dataset. The best model accuracy of 0.9382 is achieved and compared with similar state-of-the-art methods, demonstrating the superiority of the proposed framework. [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]

Synthetic images ar­­­e created using computer graphics modeling and artificial intelligence techniques, referred to as deepfakes. They modify human features by using generative models and deep learning algorithms, posing risks violations of social media regulations and spread false information. To address these concerns, the study proposed an improved generative adversarial network (GAN) model which improves accuracy while differentiating between real and fake images focusing on data augmentation and label smoothing strategies for GAN training. The study utilizes a dataset containing human faces and employs DCGAN (deep convolutional generative adversarial network) as the base model. In comparison with the traditional GANs, the proposed GAN outperform in terms of frequently used metrics i.e., Fréchet Inception Distance (FID) and accuracy. The model effectiveness is demonstrated through evaluation on the Flickr-Faces Nvidia dataset and Fakefaces d­­ataset, achieving an FID score of 55.67, an accuracy of 98.82%, and an F1-score of 0.99 in detection. This study optimizes the model parameters to achieve optimal parameter settings. This study fine-tune the model parameters to reach optimal settings, thereby reducing risks in synthetic image generation. The article introduces an effective framework for both image manipulation and detection.

With the flourishing development of generative models, image manipulation is receiving increasing attention. Rather than text modality, several elegant designs have delved into leveraging audio to manipulate images. However, existing methodologies mainly focus on image generation conditional on semantic alignment, ignoring the vivid affective information depicted in the audio. We propose an Emotion-aware StyleGAN Manipulator (EmoStyle), a framework where affective information from audio can be explicitly extracted and further utilized during image manipulation. Specifically, we first leverage the multi-modality model ImageBind for initial cross-modal retrieval between images and music, and select the music-related image for further manipulation. Simultaneously, by extracting sentiment polarity from the lyrics of the audio, we generate an emotionally rich auxiliary music branch to accentuate the affective information. We then leverage pre-trained encoders to encode audio and the audio-related image into the same embedding space. With the aligned embeddings, we manipulate the image via a direct latent optimization method. We conduct objective and subjective evaluations on the generated images, and our results show that our framework is capable of generating images with specified human emotions conveyed in the audio. [ABSTRACT FROM AUTHOR]

This article examines how the digitally manipulated family photograph functions as a means of understanding the temporal instability of the use and interpretations of photographic images. It begins by taking a close look at scholarly debates on how 'credible' the documentary value of a still photograph is, as well as how it is able to emotionally resonate with spectators. From this discussion, it becomes important to look at a key example of how an image can produce an emotional effect on a viewer; in this case, photographs of individuals' deceased family members. While exploring how this allows the spectator to reconnect with their relatives, it is also crucial to acknowledge that readings of images like these are often determined by reductive interpretations of their stillness. As the consideration of photographs as 'documents' has been contested for an extensive amount of time, it is illuminating to turn to the properties of digital photography by inspecting the photo manipulation feature 'Deep Nostalgia' on the MyHeritage app that circulated around TikTok in 2021. I look at a YouTube compilation of people reacting to seeing photographs of their family manipulated in a way that gives the impression that they are moving and emoting, alongside discussions about this in recent pop culture articles. By taking a Barthesian reading of the extended temporality of these family photographs, it is important to recognise that the connection between the subject and the image is severed both iconically and indexically from its original context. However, by understanding this photographic image in the context of being digital it must be understood differently. I will therefore use the MyHeritage phenomenon as a means of arguing that the digital image is not inferior to the 'realism' of analogue photography and must, instead, be read in relation to the history of technological change. [ABSTRACT FROM AUTHOR]

Intrinsic decomposition is a fundamental mid-level vision problem that plays a crucial role in various inverse rendering and computational photography pipelines. Generating highly accurate intrinsic decompositions is an inherently under-constrained task that requires precisely estimating continuous-valued shading and albedo. In this work, we achieve highresolution intrinsic decomposition by breaking the problem into two parts. First, we present a dense ordinal shading formulation using a shift-and scale-invariant loss in order to estimate ordinal shading cues without restricting the predictions to obey the intrinsic model. We then combine low-and high-resolution ordinal estimations using a second network to generate a shading estimate with both global coherency and local details. We encourage the model to learn an accurate decomposition by computing losses on the estimated shading as well as the albedo implied by the intrinsic model. We develop a straightforward method for generating dense pseudo ground truth using our model's predictions and multi-illumination data, enabling generalization to in-the-wild imagery. We present exhaustive qualitative and quantitative analysis of our predicted intrinsic components against state-of-the-art methods. Finally, we demonstrate the real-world applicability of our estimations by performing otherwise difficult editing tasks such as recoloring and relighting. [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]

Container technology is one of the fastest growing technologies. However, when it comes to the security of containers, their vulnerability has increased in line with its popularity. Because when users use containers, they may unknowingly perform actions that can make their Docker environment insecure. So, we need to verify security of entire container environment. In this paper, we describe a mechanism that can validate security level of container environment by using algorithmic approach. [ABSTRACT FROM AUTHOR]

Learning a disentangled representation of a distribution in a completely unsupervised way is a challenging task that has drawn attention recently. In particular, much focus has been put in separating factors of variation (i.e., attributes) within the latent code of a Generative Adversarial Network (GAN). Achieving that permits control of the presence or absence of those factors in the generated samples by simply editing a small portion of the latent code. Nevertheless, existing methods that perform very well in a noise-to-image setting often fail when dealing with a real data distribution, i.e., when the discovered attributes need to be applied to real images. However, some methods are able to extract and apply a style to a sample but struggle to maintain its content and identity, while others are not able to locally apply attributes and end up achieving only a global manipulation of the original image. In this article, we propose a completely (i.e., truly) unsupervised method that is able to extract a disentangled set of attributes from a data distribution and apply them to new samples from the same distribution by preserving their content. This is achieved by using an image-to-image GAN that maps an image and a random set of continuous attributes to a new image that includes those attributes. Indeed, these attributes are initially unknown and they are discovered during training by maximizing the mutual information between the generated samples and the attributes' vector. Finally, the obtained disentangled set of continuous attributes can be used to freely manipulate the input samples.We prove the effectiveness of our method over a series of datasets and show its application on various tasks, such as attribute editing, data augmentation, and style transfer. [ABSTRACT FROM AUTHOR]

The exponential surge in specialized image editing software has intensified visual forgery, with splicing attacks emerging as a popular forgery technique. In this context, Siamese neural networks are a remarkable tool in pattern identification for detecting image manipulations. This paper introduces a deep learning approach for splicing detection based on a Siamese neural network tailored to identifying manipulated image regions. The Siamese neural network learns unique features of specific image areas and detects tampered regions through feature comparison. This architecture employs two identical branches with shared weights and image features to compare image blocks and identify tampered areas. Subsequently, a K-means algorithm is applied to identify similar centroids and determine the precise localization of duplicated regions in the image. The experimental results encompass various splicing attacks to assess effectiveness, demonstrating a high accuracy of 98.6% and a precision of 97.5% for splicing manipulation detection. This study presents an advanced splicing image forgery detection and localization algorithm, showcasing its efficacy through comprehensive experiments.

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]

With the flourishing development of generative models, image manipulation is receiving increasing attention. Rather than text modality, several elegant designs have delved into leveraging audio to manipulate images. However, existing methodologies mainly focus on image generation conditional on semantic alignment, ignoring the vivid affective information depicted in the audio. We propose an Emotion-aware StyleGAN Manipulator (EmoStyle), a framework where affective information from audio can be explicitly extracted and further utilized during image manipulation. Specifically, we first leverage the multi-modality model ImageBind for initial cross-modal retrieval between images and music, and select the music-related image for further manipulation. Simultaneously, by extracting sentiment polarity from the lyrics of the audio, we generate an emotionally rich auxiliary music branch to accentuate the affective information. We then leverage pre-trained encoders to encode audio and the audio-related image into the same embedding space. With the aligned embeddings, we manipulate the image via a direct latent optimization method. We conduct objective and subjective evaluations on the generated images, and our results show that our framework is capable of generating images with specified human emotions conveyed in the audio.

The significance of image in advertising design has led to advancement in technological development of images. This article aims to evaluate the aesthetics, function, and implications of image manipulation in Pakistani advertising design. From limited facilities to digital advancement, the history of Pakistani design reveals a creative evolution. The advent of digital software has enabled the manipulation of photographs to effectively convey the intended messages. The research emphasizes on the analysis of contemporary advertising design of Pakistan through qualitative method. It puts forth various image manipulation techniques which are employed in contemporary advertising design. The research reflects evolving digital trends in image manipulation, and emphasizes the captivating quality of these images in engaging and influencing the consumer, thereby bolstering brand promotion. The study underscores the effective role of image manipulation in Pakistani advertising. Furthermore, advertisers and designers are recommended to consider the ethical values regarding image manipulation to ensure progress in advertising practices. [ABSTRACT FROM AUTHOR]

Images can be maliciously manipulated to hide content or duplicate certain objects. Detecting an elaborate copy-move forgery is very challenging for both humans and machines, and current methods cannot detect copy-move images with the precision required, especially for pixel-level tampered images, which is a challenge for the current existing methods. In this paper we present our own dataset (CMF58K) - the first pixel-level copy-move dataset, which consists of 580,000 images covering copy-move tampered objects in various life scenes with more than 32 object classes. Furthermore, we propose a network for detecting and locating copy-move forgeries: Efficient and Robust Identification Network (ERINet). It mainly includes four main modules: the efficient feature pyramid network (EFPN), the residual receptive field block (RRFB), the hierarchical decoding identification (HDI), and the cascaded group-reversal attention (GRA) blocks. Considering the inevitable external factors of rotation, scaling, blurring, compression and noise can hide traces of tampering and increase the difficulty of detection, we applied MaxBlurPool to our network and obtained a strong robustness. ERINet outperforms various state-of-the-art manipulation detection baselines on four image manipulation datasets. The inference speed is ∼ 49 fps on a single GPU without I/O time on the test dataset. [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]

We present a novel framework for simulating time-varying decay effects for natural images. Conventional methods assume the input image includes enough decay information and uses the color or texture information of the decayed regions to transfer its effect to the non-decayed regions. Unlike these approaches, our framework generates diverse patterns of decay effects by leveraging a decay effects generator network without referencing the decay features of the input image, which allows us to handle more general images with non-decayed objects. Our decay generator network is formed by a style-based generative adversarial network with an arbitrary-sized stationary texture generation mechanism that allows us to synthesize various sizes of decay textures. This arbitrary-sized stationary texture generation is necessary to synthesize photo-realistic decay effects since the appropriate resolutions of the decay textures depend on those of the target objects. We construct a novel decay texture image dataset that contains various types of decay texture images, such as mossy and rust, to train the decay generator network. We show that our framework is able to synthesize diverse decay effects on various non-decayed objects without using additional decayed object images.

The retouching and altering of portrait photographs used to require extreme professionalism from the artists, thus it was a privilege for photographers. However, thanks to modern image editing techniques, the process has become completely automated, to the point where it is no longer uncommon to find artificial intelligence-based software to perform portrait editing. The present research uses a self-report questionnaire to investigate the functioning of an image editing software that uses artificial intelligence to anatomically transform portrait photos beautiful at one push of a button. Participants are asked to give their opinions on pairs of photographs, one of which is an original, unedited picture, meanwhile the other is its idealised version created by the artificial intelligence-based software. The results of the research showed that the viewer's perception of automatic image retouching was not influenced by the gender or age of the model, nor by the age of the recipients. However, the participants’ first judgement of the models’ beauty influenced their attitude towards both the model and the photograph. In most cases, the photo version that participants considered more beautiful was the one they would have preferred to see both in their own social media news feed and on the cover of a magazine, furthermore, more people would have preferred to meet the model based on the photo they thought to be beautiful. The research also reveals that viewers tend to associate negative emotions with image manipulation. Although the enhancement of photographs is a phenomenon that media users regularly encounter, not much research have been conducted on the ideal of beauty in relation to AI-technology. The current study aims to contribute to filling the aforementioned research gap.

Artificial Intelligence (AI) is a phenomenon that has become embedded in human life, and this symbiotic relationship between technology and humanity is here to stay. One such use of AI is deepfakes. The use of AI for deepfakes is arguably one of the most controversial topics because it raises ethical issues. Deepfakes are images or recordings that have been convincingly altered and manipulated to misrepresent someone as doing or saying something that they did not actually do or say. These manipulations thrive in the political arena and recently in the pornography industry, in which women's faces are masked onto other bodies to create video illusions that cause non-consensual sexual-image abuse and other harms. It is no surprise that the malicious use of deepfake technology has prompted regulatory legislation like the United States National Defense Authorization Act (NDAA), and the recent ratification of amendments to the Digital Services Act (DSA) on criminalizing malicious deepfakes. Scholars, advocates, and victims continue to call for more specific and stricter laws to regulate deepfakes and assign penalties for non-adherence. This paper presents a timely analysis of deepfake pornography as a type of image-based sexual abuse, and of the position of the law on malicious use of deepfake technology. Data protection concerns under the General Data Protection Regulation, and policy recommendations and measures for redress, control, and eradication are also addressed. [ABSTRACT FROM AUTHOR]

Image forensics is an investigation of digital images to identify manipulations that have been done on them. Nowadays, due to the availability of different low-cost devices for capturing images, digital images are gaining quite a bit of popularity. It occurs frequently that these images are manipulated by mistake or on purpose, resulting in inaccurate information being presented by the image. There is a need to develop techniques to identify forgeries present in digital images used by the media, in court trials, and for maintaining visual records, since digital images are commonly used as evidence through the media, in court, and for maintaining record keeping. A detailed review of various image forgery detection techniques is presented in this article including comparisons between the various methods, pros and cons, and results obtained during the experimentation. [ABSTRACT FROM AUTHOR]

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

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]

Rapid progress in digitisation and computer techniques have enabled noteworthy new pottery analysis applications in recent decades. We focus on analytical techniques directed specifically at archaeological pottery research in this survey and review the specific benefits these have brought in the field. We consider techniques based on heterogeneous sources such as drawings, photographs, 3D scans and CT volume data. The various approaches and methods are structured according to the main steps in pottery processing in archaeology: documentation, classification and retrieval. Within these categories we review the most relevant papers and identify their advantages and limitations. We evaluate both freely and commercially available analysis tools and databases. Finally, we discuss open problems and future challenges in the field of pottery analysis. [ABSTRACT FROM AUTHOR]

Image manipulation of the human face is a trending topic of image forgery, which is done by transforming or altering face regions using a set of techniques to accomplish desired outputs. Manipulated face images are spreading on the internet due to the rise of social media, causing various societal threats. It is challenging to detect the manipulated face images effectively because (i) there has been a limited number of manipulated face datasets because most datasets contained images generated by GAN models; (ii) previous studies have mainly extracted handcrafted features and fed them into machine learning algorithms to perform manipulated face detection, which was complicated, error-prone, and laborious; and (iii) previous models failed to prove why their model achieved good performances. In order to address these issues, this study introduces a large face manipulation dataset containing vast variations of manipulated images created and manually validated using various manipulation techniques. The dataset is then used to train a fine-tuned RegNet model to detect manipulated face images robustly and efficiently. Finally, a manipulated region analysis technique is implemented to provide some in-depth insights into the manipulated regions. The experimental results revealed that the RegNet model showed the highest classification accuracy of 89% on the proposed dataset compared to standard deep learning models. [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]