Real Face Research Articles

Discrete Fourier Transform in Unmasking Deepfake Images: A Comparative Study of StyleGAN Creations

This study proposes a novel forgery detection method based on the analysis of frequency components of images using the Discrete Fourier Transform (DFT). In recent years, face manipulation technologies, particularly Generative Adversarial Networks (GANs), have advanced to such an extent that their misuse, such as creating deepfakes indistinguishable to human observers, has become a significant societal concern. We reviewed two GAN architectures, StyleGAN and StyleGAN2, generating synthetic faces that were compared with real faces from the FFHQ and CelebA-HQ datasets. The key results demonstrate classification accuracies above 99%, with F1 scores of 99.94% for Support Vector Machines and 97.21% for Random Forest classifiers. These findings underline the fact that performing frequency analysis presents a superior approach to deepfake detection compared to traditional spatial detection methods. It provides insight into subtle manipulation cues in digital images and offers a scalable way to enhance security protocols amid rising digital impersonation threats.

The Portrayals of the Other in Yemeni Postcolonial Fiction: A Critical Study on Ahmed Zein's Steamer Point

This research paper investigates the portrayals of the other in Ahmed Zein’s Steamer Point (2015), with a special focus on the European colonizer. In addition to that, it examines the coexistence between the natives and Europeans as reflected in the story. The postcolonial theory has been used as a methodology to frame the discussions and analysis of the novel in this study. The paper has been constructed in five sections: Introduction, background of the study, theoretical framework, discussions and analysis, and finally the conclusion. The study ended up with the conclusion that The relationship between the self and the other is one of the complicated issues in the postcolonial era. Each one of self and other seeks to abolish the boundaries between them. The other seeks to abolish the components of the self, which make the self feels the loss of his privacy and rights in his relationship with the other. Steamer Point dealt with the issue of colonization and the relationship between the colonizer and the colonized people. While the story seems to glorify the colonizers, it also showed their real face in the negative way they looked at the natives, as well as how badly they treated them. That hegemony and biasness against the local people led at the end of the story to independence.

ExpAvatar: High-Fidelity Avatar Generation of Unseen Expressions with 3D Face Priors

The reconstruction of dynamic head avatars has gained increasing significance, giving rise to various downstream applications such as visual dubbing and digital human creation. Despite recent advancements, generating novel, unseen expressions for a given identity remains challenging in concurrently achieving 1) accurate expression and consistent appearance and 2) high-quality and realistic faces. This paper introduces ExpAvatar, a novel approach crafted to address these challenges. ExpAvatar elaborately leverages the appearance consistency capabilities inherent in 3DMMs-based models along with the robust generalization ability of DDPMs-based models to alleviate appearance drift issues and enhance the generation of unseen expressions. Specifically, ExpAvatar introduces a Face Priors-conditioned Diffusion model (FPDiff) to inject 3D face priors into generation models through fine-tuning. Furthermore, a Face Priors-conditioned Catalyst (FPCatalyst) is employed to enhance the inference efficiency and generation quality. Moreover, we propose a unique confidence-based regularizer function to mitigate the effect of imperfect face-tracking estimates, thereby improving the quality of dynamic neural head avatars. Experimental results demonstrate that ExpAvatar surpasses current state-of-the-art solutions in generating unseen expressions, marking an advancement in the realm of dynamic head avatar synthesis. Code: https://github.com/yuangan/ExpAvatar .

Inefficient and unique processing of social-emotional interference in school-aged children with high-functioning autism spectrum disorder.

Interest is growing in investigating the ability of children with autism spectrum disorder (ASD) to process social information under conflicting and complex environments. However, few studies have employed objective behavioral measures to directly explore the underlying profile of social-emotional interference control. In the current study, 53 children with ASD and 53 typically developing (TD) control, aged 6-12 years, completed a set of modified flanker tasks involving arrows, schematic faces, same real faces (with facial interference by the same person), and different real faces (with facial interference by different people), respectively. Response time in incongruent (RTInc) and congruent conditions (RTCon), percentage of errors in incongruent (%ErrorInc) and congruent conditions (%ErrorCon), and flanker effect calculated by ΔRT = (RTInc - RTCon)/RTCon and Δ%Error = %ErrorInc - %ErrorCon were used as outcome metrics. We obtained three major results: (1) the ASD group had longer RTInc and RTCon compared to the TD group in the arrow, schematic-face, and same real-face tasks; (2) compared with the performance in the arrow flanker task, both groups exhibited longer RTs and reduced ΔRTs in the same real-face task; however, in the schematic-face task, longer RT and reduced ΔRT were exhibited in the TD group, but not in the ASD group; and (3) in the different real-face task, ASD group had higher %Error than the TD group, and %Error was negatively correlated with RT only in the ASD group. The current study delineates the inefficient processing of social-emotional interference in school-aged children with ASD and further suggests that these children might adopt a relatively optimized strategy like symbolization when dealing with emotional conflict. However, such compensatory cognitive strategies may be exhausted along with the increase in information load. These findings provide new perspectives of considering the difference more than difficulty in the cognitive profile of ASD, which will benefit the development of targeted behavioral interventions.

Face and face pareidolia in patients with temporal lobe epilepsy indicates different neural processing: an event-related potential study

BackgroundVisual perception of face images or face pareidolia can be evaluated with event-related potentials (ERP) for healthy subjects and patients with neurological conditions. In this study, we aimed to analyse event-related potential components such as P100, N100, N170, and vertex-positive potential (VPP) in response to face pareidolia perception in temporal lobe epilepsy (TLE) patients.MethodsERPs were recorded during the pareidolia test. Waveforms were analzyed and current source density (CSD) maps were generated.ResultsCSD profiles were shown to be interpretable when face and face pareidolia conditions. N100, P100, and N170 components showed larger amplitudes and longer latency in epilepsy patients in response to face pareidolia stimuli compared to real face images. However, the N170 component latency did not differ significantly between epilepsy patients and healthy participants, while the larger amplitude and longer latency of N100 and P100 responses were evoked in healthy patients.ConclusionsOur results indicate a difference in the neural mechanisms of processing real face information and pareidolia face-like information in TLE patients.

FaceDefend: Copyright Protection to Prevent Face Embezzle

With the rapid evolution of deep learning and the advent of artificial intelligence, the metaverse has emerged as a significant technology. Within the metaverse, diverse elements such as rich applications and realistic digital avatars provide users with immersive experiences, but it poses a series of security problems. Current research predominantly focuses on the data storage and transmission processes from the perspective of blockchain and the Internet of Things to achieve the protection of the metaverse. However, there exists a gap in security research on the digital avatar generation process. Given that digital avatars are the primary entities engaging in social activities within the metaverse and are crafted based on real face images, the virtual character can be generated easily by stealing the user’s face image and controlled to interact with others. In order to deal with the above problems, we propose a novel method to prevent the misuse of faces, which maintains the security of the metaverse by protecting facial data and thus preventing its misuse. We explore the common architecture of generative models and propose a defense method based on copyright protection to prevent face embezzling. Firstly, we utilize the copyright protection module to obtain copyright protection information. Secondly, we utilized the defense control module to ensure the representation of the protected images occurs errors in the latent space of the generation model. Therefore the subsequent generation task output fails, which effectively protects the face data and prevents the generation of digital avatars. Furthermore, the results on public datasets and across multiple generative models present unnatural outputs, indicating the excellence of our defense and transfer capabilities.

Faceify- Human Face Sketch to Real

The Human Face Sketch to Real Image Application using deep learning presents the development of an innovative application designed to convert human face sketches into realistic image using advanced deep learning techniques. The primary objective of this research is to bridge the gap between manual sketching and digital realism, offering a powerful tool for artists, law enforcement, and digital content creators. The proposed system leverages generative adversarial networks (GANs), particularly a variant known as the Sketch-to-Image GAN (SI-GAN), which is optimizes for translating sparse and abstract sketch lines into high-fidelity facial representations. The architecture consists of a generator network that refines sketches into realistic images, and a discriminator network that distinguishes between generated images and real photographs, continuously enhancing the model’s output. To ensure high accuracy and detail preservation, the model is trained on a diverse dataset comprising thousands of paired sketch and real face images. Advanced techniques such as attention mechanisms and multi-scale feature extraction are incorporated to maintain the integrity of facial features and textures. Additionally, a user friendly interface has been developed, allowing users to input sketches and receive high-quality realistic images in real-time. Evaluation metrics, including structural similarity index (SSIM) and Frechet Inception Distance (FID), Indicate that the proposed model outperforms existing state-of-the-art methods in both quality and realism. Keywords: Image Translation, Conditional Generative Adversarial Networks (CGANs), Deep Learning, Mobile Application Development, Backend Integration, Face Sketch, Photorealistic Image.

Can You Tell Real from Fake Face Images? Perception of Computer-Generated Faces by Humans

With recent advances in machine learning and big data, it is now possible to create synthetic images that look real. Face generation is often of particular interest, as faces can be used for various purposes. However, improper use of such content can lead to the dissemination of false information, such as fake news, and thus pose a threat to society. This work studies whether people believe the truthfulness of faces using eye tracking and self-reports, including free-form textual explanations when participants encounter real and computer-generated faces. We used three different datasets for our evaluations, and our experimental results show that while people are relatively better at identifying the truthfulness of real faces and faces generated by earlier machine learning algorithms with different gazing behaviors in viewing and rating phases, they perform less accurately when deciding the truthfulness of synthetic face images that are generated by newer algorithms. Our findings provide important insights for society and policymakers.

Dynamic Synthetic Faces Improve the Intelligibility of Noisy Speech, But Not As Much As Real Faces

Dynamic Synthetic Faces Improve the Intelligibility of Noisy Speech, But Not As Much As Real Faces

FaReT 2.1: Anatomically precise manipulation of race in 3D face models and a pipeline to import real face scans

FaReT 2.1: Anatomically precise manipulation of race in 3D face models and a pipeline to import real face scans

Factors Associated With Pain Perception and Management Among Paediatric Nurses and Nursing Students Using Virtual Children: A Comparative Study.

To identify and compare factors associated with pain perception and management among paediatric nurses and nursing students using virtual human technology. This study employed a comparative, cross-sectional design. Fifty-one paediatric nurses from a tertiary hospital and 50 senior nursing students from a nursing college, both located in South Korea, participated in the study. Virtual vignettes, including scenarios with virtual children and their clinical information, were used to assess participants' pain perception and management. The virtual children, created from real face photos, varied in age (young/old), sex (boy/girl) and facial expressions (smile/grimace). Participants rated perceived pain scores and selected management choices for eight virtual vignettes, which were randomly presented. The Korean version of the Paediatric Nurses' Knowledge and Attitude Survey was completed along with demographic information. Both paediatric nurses and nursing students rated pain scores lower than the actual pain scores reported by virtual children. Nurses rated higher pain scores and accordingly selected more medication doses compared to students. Beyond pain-related knowledge, the facial expression of the virtual child influenced pain rating and medication choices, while age and sex had no effect. Nursing students were more sensitive to the facial expressions of virtual children when rating their pain compared to nurses. Future studies should explore how paediatric nurses' clinical experiences impact their assessment and management of child patient pain. Paediatric nurses' knowledge of pain is inadequate, necessitating ongoing education in pain management. Both nurses and nursing students should self-reflect on their pain assessment practices to ensure unbiased care regardless of child patients' characteristics. Virtual human technology can be utilised to train nurses and students to identify and address any biases in their assessment of patients' pain perception. STROBE checklist, cross-sectional. Digital face photos of four healthy children were used as the fundamental material for creating virtual children, with their parents' consent.

EEG correlates of static and dynamic face perception: The role of naturalistic motion

Much of our understanding of how the brain processes dynamic faces comes from research that compares static photographs to dynamic morphs, which exhibit simplified, computer-generated motion. By comparing static, video recorded, and dynamic morphed expressions, we aim to identify the neural correlates of naturalistic facial dynamism, using time-domain and time-frequency analysis. Dynamic morphs were made from the neutral and peak frames of video recorded transitions of happy and fearful expressions, which retained expression change and removed asynchronous and non-linear features of naturalistic facial motion. We found that dynamic morphs elicited increased N400 amplitudes and lower LPP amplitudes compared to other stimulus types. Video recordings elicited higher LPP amplitudes and greater frontal delta activity compared to other stimuli. Thematic analysis of participant interviews using a large language model revealed that participants found it difficult to assess the genuineness of morphed expressions, and easier to analyse the genuineness of happy compared to fearful expressions. Our findings suggest that animating real faces with artificial motion may violate expectations (N400) and reduce the social salience (LPP) of dynamic morphs. Results also suggest that delta oscillations in the frontal region may be involved with the perception of naturalistic facial motion in happy and fearful expressions. Overall, our findings highlight the sensitivity of neural mechanisms required for face perception to subtle changes in facial motion characteristics, which has important implications for neuroimaging research using faces with simplified motion.

DIGAN: distillation model for generating 3D-aware Terracotta Warrior faces

Utilizing Generative Adversarial Networks (GANs) to generate 3D representations of the Terracotta Warriors offers a novel approach for the preservation and restoration of cultural heritage. Through GAN technology, we can produce complete 3D models of the Terracotta Warriors’ faces, aiding in the repair of damaged or partially destroyed figures. This paper proposes a distillation model, DIGAN, for generating 3D Terracotta Warrior faces. By extracting knowledge from StyleGAN2, we train an innovative 3D generative network. G2D, the primary component of the generative network, produces detailed and realistic 2D images. The 3D generator modularly decomposes the generation process, covering texture, shape, lighting, and pose, ultimately rendering 2D images of the Terracotta Warriors’ faces. The model enhances the learning of 3D shapes through symmetry constraints and multi-view data, resulting in high-quality 2D images that closely resemble real faces. Experimental results demonstrate that our method outperforms existing GAN-based generation methods.

Pupil dilation reflects the social and motion content of faces.

Human facial features (eyes, nose, and mouth) allow us to communicate with others. Observing faces triggers physiological responses, including pupil dilation. Still, the relative influence of social and motion content of a visual stimulus on pupillary reactivity has never been elucidated. A total of 30 adults aged 18-33 years old were recorded with an eye tracker. We analysed the event-related pupil dilation in response to stimuli distributed along a gradient of social salience (non-social to social, going from objects to avatars to real faces) and dynamism (static to micro- to macro-motion). Pupil dilation was larger in response to social (faces and avatars) compared to non-social stimuli (objects), with surprisingly a larger response for avatars. Pupil dilation was also larger in response to macro-motion compared to static. After quantifying each stimulus' real quantity of motion, we found that the higher the quantity of motion, the larger the pupil dilated. However, the slope of this relationship was not higher for social stimuli. Overall, pupil dilation was more sensitive to the real quantity of motion than to the social component of motion, highlighting the relevance of ecological stimulations. Physiological response to faces results from specific contributions of both motion and social processing.

Impaired inhibitory control when processing real but not cartoon emotional faces in autistic children: Evidence from an event-related potential study.

Impaired socioemotional functioning characterizes autistic children, but does weak inhibition control underlie their socioemotional difficulty? This study addressed this question by examining whether and, if so, how inhibition control is affected by face realism and emotional valence in school-age autistic and neurotypical children. Fifty-two autistic and 52 age-matched neurotypical controls aged 10-12 years completed real and cartoon emotional face Go/Nogo tasks while event-related potentials (ERPs) were recorded. The analyses of inhibition-emotion components (i.e., N2, P3, and LPP) and a face-specific N170 revealed that autistic children elicited greater N2 while inhibiting Nogo trials and greater P3/LPP and late LPP for real but not cartoon emotional faces. Moreover, autistic children exhibited a reduced N170 to real face emotions only. Furthermore, correlation results showed that better behavioral inhibition and emotion recognition in autistic children were associated with a reduced N170. These findings suggest that neural mechanisms of inhibitory control in autistic children are less efficient and more disrupted during real face processing, which may affect their age-appropriate socio-emotional development.

Detection of Deepfake Videos Using Long-Distance Attention.

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video content and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this article, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and interframe inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces from a global perspective, respectively. The two components are designed using a novel long-distance attention mechanism. One component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves state-of-the-art performance, and the proposed long-distance attention method can effectively capture pivotal parts for face forgery.

DAFNet: A Dual Attention Fusion-Based Face Super-Resolution Network

With the development of convolutional neural networks (CNNs), deep learning-based face super-resolution (FSR) approaches have achieved remarkable results in recent years. However, existing FSR methods often rely on face prior knowledge, which greatly increases network complexity and computation. In this paper, we propose DAFNet, a dual attention fusion-based FSR network comprising numerous face attention fusion modules (FAFMs). FAFM is a residual structure containing attention mechanism, which is divided into feature branches and attention branches. The feature branch introduces an hourglass block to extract multi-scale information, while the attention branch incorporates channel attention and spatial attention in series. This design ensures that the network prioritizes important information and effectively recovers detailed facial features. Luminance-chrominance error loss and gradient loss are introduced to guide the training process. Additionally, adversarial loss and perceptual loss are incorporated to enhance the recovery of visually realistic face images. Notably, our method can produce clear faces at a high-scale factor of eight times without relying on any facial prior information, effectively reducing network complexity. Quantitative and qualitative experiments conducted on the CelebA and Helen datasets underscore the effectiveness of the proposed model.

JRC: Deepfake detection via joint reconstruction and classification

Deep learning has enabled realistic face manipulation for malicious purposes (e.g., deepfakes), which poses significant concerns over the integrity of the media in circulation. Most existing deep learning techniques for deepfake detection can achieve promising performance in the intra-dataset evaluation setting, but are unable to perform satisfactorily in the inter-dataset evaluation setting. Most previous methods use a backbone network to extract global features for making predictions and only employ binary supervision to train the network. Classification merely based on the learning of global features often leads to weak generalizability to deepfakes of unseen manipulation methods. In this paper, we design a two-branch Convolutional AutoEncoder (CAE), which considers the reconstruction and classification tasks simultaneously for deepfake detection. This Joint Reconstruction and Classification (JRC) method shares the information learned by one task with the other, each focusing on different aspects, and hence boosts the overall performance. JRC is end-to-end, and experiments demonstrate that it achieves state-of-the-art performance on three commonly-used datasets, particularly in the cross-dataset evaluation setting.

AI-generated faces show lower morphological diversity than real faces do

Some recent studies suggest that artificial intelligence can create realistic human faces subjectively unrecognizable from faces of real people. We have compared static facial photographs of 197 real men with a sample of 200 male faces generated by artificial intelligence to test whether they converge in basic morphological characteristic such as shape variation and bilateral asymmetry. Both datasets depicted standardized faces of European men with a neutral expression. Then we used geometric morphometrics to investigate their facial morphology and calculate the measures of shape variation and asymmetry. We found that the natural faces of real individuals were more variable in their facial shape than the artificially generated faces were. Moreover, the artificially synthesized faces showed lower levels of facial asymmetry than the control group. Despite the rapid development of generative adversarial networks, natural faces are thus still statistically distinguishable from the artificial ones by objective measurements. We recommend the researchers in face perception, that aim to use artificially generated faces as ecologically valid stimuli, to check whether their stimuli morphological variance is comparable with that of natural faces in a target population.

Impaired social reward processing in individuals with Internet gaming disorder and its relationship with early face perception

Previous research has found that individuals with Internet gaming disorder (IGD) show different patterns of social function impairments in game-related and real-life social contexts. Impaired social reward processing may be the underlying mechanism according to the Social Motivation Theory. Thus, in this study, event-related potentials were recorded from 24 individuals with IGD and 24 healthy gamers during a social judgement task. We focused on reward positivity (RewP) elicited by game-related and real-life social rewards, and N170 elicited by game avatar faces and real faces. These indicators were used to explore the neurocognitive mechanism of impaired social reward processing in individuals with IGD and its relationship with early face perception. Results showed that (1) the RewP elicited by real-life social reward was considerably reduced in individuals with IGD relative to healthy gamers. (2) The N170 elicited by game avatar faces in individuals with IGD was larger than that elicited by real faces. However, the N170 was not associated with RewP in either group. (3) The score for IGD severity was correlated with the RewP elicited by real-life social reward and the N170 elicited by game avatar face. In conclusion, the present study suggests that the impaired social reward processing in individuals with IGD is mainly manifested in a decreased neural sensitivity to real-life social reward. Meanwhile, the reduced RewP elicited by real-life social reward and the enhanced N170 elicited by game avatar face might serve as potential biomarkers for IGD.