Phase change plays a critical role in thermal management systems, yet quantitative characterization of multiphase heat transfer remains limited by the challenges of measuring temperature fields in chaotic, rapidly evolving flow regimes. While computational methods offer spatiotemporal resolution in idealized cases, replicating complex experimental conditions remains prohibitively difficult. Here, we present a data-driven framework that leverages a conditional generative adversarial network (CGAN) to infer temperature fields from geometric phase contours in a canonical pool boiling configuration where advanced data collection techniques are restricted. Using high-speed imaging data and simulation-informed training, our model demonstrates the ability to reconstruct temperature fields with errors below 6%. We further show that standard data augmentation strategies are effective in enhancing both accuracy and physical plausibility of the predicted maps across both simulation and experimental datasets when precise physical constraints are not applicable. Our results highlight the potential of deep generative models to bridge the gap between observable multiphase phenomena and underlying thermal transport, offering a powerful approach to augment and interpret experimental measurements in complex two-phase systems.
The increasing integration of inverter-based resources (IBRs) and communication networks has brought both modernization and new vulnerabilities to the power system infrastructure. These vulnerabilities expose the system to internal faults and cyber threats, particularly False Data Injection (FDI) attacks, which can closely mimic real fault scenarios. Hence, this work presents a two-stage fault and cyberattack detection framework tailored for inverter-based microgrids. Stage 1 introduces an unsupervised learning model Feature Feedback Generative Adversarial Network (F2GAN), to distinguish between genuine internal faults and cyber-induced anomalies in microgrids. Compared to conventional GAN architectures, F2GAN demonstrates improved system diagnosis and greater adaptability to zero-day attacks through its feature-feedback mechanism. In Stage 2, supervised machine learning techniques, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Decision Trees (DT), and Artificial Neural Networks (ANN) are applied to localize and classify faults within inverter switches, distinguishing between single-switch and multi-switch faults. The proposed framework is validated on a simulated microgrid environment, illustrating robust performance in detecting and classifying both physical and cyber-related disturbances in power electronic-dominated systems.
Voice conversion (VC) stands as a crucial research area in speech synthesis, enabling the transformation of a speaker's vocal characteristics to resemble another while preserving the linguistic content. This technology has broad applications, including automated movie dubbing, speech-to-singing conversion, and assistive devices for pathological speech rehabilitation. With the increasing demand for high-quality and natural-sounding synthetic voices, researchers have developed a wide range of VC techniques. Among these, generative adversarial network (GAN)-based approaches have drawn considerable attention for their powerful feature-mapping capabilities and potential to produce highly realistic speech. Despite notable advancements, challenges such as ensuring training stability, maintaining linguistic consistency, and achieving perceptual naturalness continue to hinder progress in GAN-based VC systems. This systematic review presents a comprehensive analysis of the voice conversion landscape, highlighting key techniques, key challenges, and the transformative impact of GANs in the field. The survey categorizes existing methods, examines technical obstacles, and critically evaluates recent developments in GAN-based VC. By consolidating and synthesizing research findings scattered across the literature, this review provides a structured understanding of the strengths and limitations of different approaches. The significance of this survey lies in its ability to guide future research by identifying existing gaps, proposing potential directions, and offering insights for building more robust and efficient VC systems. Overall, this work serves as an essential resource for researchers, developers, and practitioners aiming to advance the state-of-the-art (SOTA) in voice conversion technology.
The demand for realistic virtual immersive audio continues to grow, with Head-Related Transfer Functions (HRTFs) playing a key role. HRTFs capture how sound reaches our ears, reflecting unique anatomical features and enhancing spatial perception. It has been shown that personalized HRTFs improve localization accuracy, but their measurement remains time-consuming and requires a noise-free environment. Although machine learning has been shown to reduce the required measurement points and, thus, the measurement time, a controlled environment is still necessary. This paper proposes a method to address this constraint by presenting a novel technique that can upsample sparse, noisy HRTF measurements. The proposed approach combines an HRTF Denoisy U-Net for denoising and an Autoencoding Generative Adversarial Network (AE-GAN) for upsampling from three measurement points. The proposed method achieves a log-spectral distortion (LSD) error of 5.41 dB and a cosine similarity loss of 0.0070, demonstrating the method's effectiveness in HRTF upsampling.
Distributed Denial of Service (DDoS) attacks represent a persistent and evolving threat to modern networked systems, capable of causing large-scale service disruptions. The complexity of such attacks, often hidden within high-dimensional and redundant network traffic data, necessitates robust and intelligent feature selection techniques for effective detection. Traditional methods such as filter-based, wrapper-based, and embedded approaches, each offer strengths but struggle with scalability or adaptability in complex attack environments. In this study, we explore these existing techniques through a detailed comparative analysis and highlight their limitations when applied to large-scale DDoS detection tasks. Building upon these insights, we introduce a novel Generative Adversarial Network-based Feature Selection (GANFS) method that leverages adversarial learning dynamics to identify the most informative features. By training a GAN exclusively on attack traffic and employing a perturbation-based sensitivity analysis on the Discriminator, GANFS effectively ranks feature importance without relying on full supervision. Experimental evaluations using the CIC-DDoS2019 dataset demonstrate that GANFS not only improves the accuracy of downstream classifiers but also enhances computational efficiency by significantly reducing feature dimensionality. These results point to the potential of integrating generative learning models into cybersecurity pipelines to build more adaptive and scalable detection systems.
This work empirically evaluates machine learning models on two imbalanced public datasets (KDDCUP99 and Credit Card Fraud 2013). The method includes data preparation, model training, and evaluation, using an 80/20 (train/test) split. Models tested include eXtreme Gradient Boosting (XGB), Multi Layer Perceptron (MLP), Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Multiple-Objective Generative Adversarial Active Learning (MO-GAAL), with XGB and MLP further combined with Random-Over-Sampling (ROS) and Self-Paced-Ensemble (SPE). Evaluation involves 5-fold cross-validation and imputation techniques (mean, median, and IterativeImputer) with 10, 20, 30, and 50 % missing data. Findings show XGB and MLP outperform generative models. IterativeImputer results are comparable to mean and median, but not recommended for large datasets due to increased complexity and execution time. The code used is publicly available on GitHub (github.com/markushaug/acr-25).
This paper introduces DeeCLIP, a novel framework for detecting AI-generated images using CLIP-ViT and fusion learning. Despite significant advancements in generative models capable of creating highly photorealistic images, existing detection methods often struggle to generalize across different models and are highly sensitive to minor perturbations. To address these challenges, DeeCLIP incorporates DeeFuser, a fusion module that combines high-level and low-level features, improving robustness against degradations such as compression and blurring. Additionally, we apply triplet loss to refine the embedding space, enhancing the model's ability to distinguish between real and synthetic content. To further enable lightweight adaptation while preserving pre-trained knowledge, we adopt parameter-efficient fine-tuning using low-rank adaptation (LoRA) within the CLIP-ViT backbone. This approach supports effective zero-shot learning without sacrificing generalization. Trained exclusively on 4-class ProGAN data, DeeCLIP achieves an average accuracy of 89.00% on 19 test subsets composed of generative adversarial network (GAN) and diffusion models. Despite having fewer trainable parameters, DeeCLIP outperforms existing methods, demonstrating superior robustness against various generative models and real-world distortions. The code is publicly available at https://github.com/Mamadou-Keita/DeeCLIP for research purposes.
After demonstrating significant success in image synthesis, Generative Adversarial Network (GAN) models have likewise made significant progress in the field of speech synthesis, leveraging their capacity to adapt the precise distribution of target data through adversarial learning processes. Notably, in the realm of State-Of-The-Art (SOTA) GAN-based Voice Conversion (VC) models, there exists a substantial disparity in naturalness between real and GAN-generated speech samples. Furthermore, while many GAN models currently operate on a single generator discriminator learning approach, optimizing target data distribution is more effectively achievable through a single generator multi-discriminator learning scheme. Hence, this study introduces a novel GAN model named Collective Learning Mechanism-based Optimal Transport GAN (CLOT-GAN) model, incorporating multiple discriminators, including the Deep Convolutional Neural Network (DCNN) model, Vision Transformer (ViT), and conformer. The objective of integrating various discriminators lies in their ability to comprehend the formant distribution of mel-spectrograms, facilitated by a collective learning mechanism. Simultaneously, the inclusion of Optimal Transport (OT) loss aims to precisely bridge the gap between the source and target data distribution, employing the principles of OT theory. The experimental validation on VCC 2018, VCTK, and CMU-Arctic datasets confirms that the CLOT-GAN-VC model outperforms existing VC models in objective and subjective assessments.
Generative Adversarial Network (GAN) inversion have demonstrated excellent performance in image inpainting that aims to restore lost or damaged image texture using its unmasked content. Previous GAN inversion-based methods usually utilize well-trained GAN models as effective priors to generate the realistic regions for missing holes. Despite excellence, they ignore a hard constraint that the unmasked regions in the input and the output should be the same, resulting in a gap between GAN inversion and image inpainting and thus degrading the performance. Besides, existing GAN inversion approaches often consider a single modality of the input image, neglecting other auxiliary cues in images for improvements. Addressing these problems, we propose a novel GAN inversion approach, dubbed MMInvertFill, for image inpainting. MMInvertFill contains primarily a multimodal guided encoder with a pre-modulation and a GAN generator with F&W+ latent space. Specifically, the multimodal encoder aims to enhance the multi-scale structures with additional semantic segmentation edge texture modalities through a gated mask-aware attention module. Afterwards, a pre-modulation is presented to encode these structures into style vectors. To mitigate issues of conspicuous color discrepancy and semantic inconsistency, we introduce the F&W+ latent space to bridge the gap between GAN inversion and image inpainting. Furthermore, in order to reconstruct faithful and photorealistic images, we devise a simple yet effective Soft-update Mean Latent module to capture more diversified in-domain patterns for generating high-fidelity textures for massive corruptions. In our extensive experiments on six challenging datasets, we show that our MMInvertFill qualitatively and quantitatively outperforms other state-of-the-arts and it supports the completion of out-of-domain images effectively.
The advancement of advanced air mobility (AAM) in recent years has given rise to the concept of low-altitude economy (LAE). However, the diverse flight activities associated with the emerging LAE applications in urban scenarios confront complex physical environments, which urgently necessitates ubiquitous and reliable communication to guarantee the operation safety of the low-altitude aircraft. As one of promising technologies for the sixth generation (6G) mobile networks, channel knowledge map (CKM) enables the environment-aware communication by constructing a site-specific dataset, thereby providing a priori on-site information for the aircraft to obtain the channel state information (CSI) at arbitrary locations with much reduced online overhead. Diverse base station (BS) deployments in the three-dimensional (3D) urban low-altitude environment require efficient 3D CKM construction to capture spatial channel characteristics with less overhead. Towards this end, this paper proposes a 3D channel gain map (CGM) inference method based on a 3D conditional generative adversarial network (3D-CGAN). Specifically, we first analyze the potential deployment types of BSs in urban low-altitude scenario, and investigate the CGM representation with the corresponding 3D channel gain model. The framework of the proposed 3D-CGAN is then discussed, which is trained by a dataset consisting of existing CGMs. Consequently, the trained 3D-CGAN is capable of inferring the corresponding CGM only based on the BS coordinate without additional measurement. The simulation results demonstrate that the CGMs inferred by the proposed 3D-CGAN outperform those of the benchmark schemes, which can accurately reflect the radio propagation condition in 3D environment.
To the best of our knowledge, all existing methods that can generate synthetic brain magnetic resonance imaging (MRI) scans for a specific individual require detailed structural or volumetric information about the individual's brain. However, such brain information is often scarce, expensive, and difficult to obtain. In this paper, we propose the first approach capable of generating synthetic brain MRI segmentations -- specifically, 3D white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) segmentations -- for individuals using their easily obtainable and often readily available demographic, interview, and cognitive test information. Our approach features a novel deep generative model, CSegSynth, which outperforms existing prominent generative models, including conditional variational autoencoder (C-VAE), conditional generative adversarial network (C-GAN), and conditional latent diffusion model (C-LDM). We demonstrate the high quality of our synthetic segmentations through extensive evaluations. Also, in assessing the effectiveness of the individual-specific generation, we achieve superior volume prediction, with mean absolute errors of only 36.44mL, 29.20mL, and 35.51mL between the ground-truth WM, GM, and CSF volumes of test individuals and those volumes predicted based on generated individual-specific segmentations, respectively.
In this paper, we propose a new adversarial training framework to address high-dimensional instantaneous channel estimation in wireless communications. Specifically, we train a generative adversarial network to predict a channel realization in the time-frequency-space domain, in which the generator exploits the third-order moment of the input in its loss function and applies a new reparameterization method for latent distribution learning to minimize the Wasserstein distance between the true and estimated channel distributions. Next, we propose an explainable artificial intelligence mechanism to examine how the critic discriminates the generated channel. We demonstrate that our proposed framework is superior to existing methods in terms of minimizing estimation errors. Additionally, we find that the critic's attention focuses on the high-power portion of the channel's time-frequency representation.
A common dilemma while photographing a scene is whether to capture it at a wider angle, allowing more of the scene to be covered but in less detail or to click in a narrow angle that captures better details but leaves out portions of the scene. We propose a novel method in this paper that infuses wider shots with finer quality details that is usually associated with an image captured by the primary lens by capturing the same scene using both narrow and wide field of view (FoV) lenses. We do so by training a Generative Adversarial Network (GAN)-based model to learn to extract the visual quality parameters from a narrow-angle shot and to transfer these to the corresponding wide-angle image of the scene using residual connections and an attention-based fusion module. We have mentioned in details the proposed technique to isolate the visual essence of an image and to transfer it into another image. We have also elaborately discussed our implementation details and have presented the results of evaluation over several benchmark datasets and comparisons with contemporary advancements in the field.
In the era of Industry 4.0, ensuring the resilience of cyber-physical systems against sophisticated cyber threats is increasingly critical. This study proposes a pioneering AI-based control framework that enhances short-term voltage stability assessments (STVSA) in power systems under complex composite cyber-attacks. First, by incorporating white-box and black-box adversarial attacks with Denial-of-Service (DoS) perturbations during training, composite adversarial attacks are implemented. Second, the application of Spectral Normalized Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (SNCWGAN-GP) and Fast Gradient Sign Method (FGSM) strengthens the model's resistance to adversarial disturbances, improving data quality and training stability. Third, an assessment model based on Long Short-Term Memory (LSTM)-enhanced Graph Attention Network (L-GAT) is developed to capture dynamic relationships between the post-fault dynamic trajectories and electrical grid topology. Experimental results on the IEEE 39-bus test system demonstrate the efficacy and superiority of the proposed method in composite cyber-attack scenarios. This contribution is pivotal to advancing AI-based resilient control strategies for nonlinear dynamical systems, marking a substantial enhancement in the security of cyber-physical systems.
Remote photoplethysmography (rPPG) offers a novel approach to noninvasive monitoring of vital signs, such as respiratory rate, utilizing a camera. Although several supervised and self-supervised methods have been proposed, they often fail to accurately reconstruct the PPG signal, particularly in distinguishing between systolic and diastolic components. Their primary focus tends to be solely on extracting heart rate, which may not accurately represent the complete PPG signal. To address this limitation, this paper proposes a novel deep learning architecture using Generative Adversarial Networks by introducing multi-discriminators to extract rPPG signals from facial videos. These discriminators focus on the time domain, the frequency domain, and the second derivative of the original time domain signal. The discriminator integrates four loss functions: variance loss to mitigate local minima caused by noise; dynamic time warping loss to address local minima induced by alignment and sequences of variable lengths; Sparsity Loss for heart rate adjustment, and Variance Loss to ensure a uniform distribution across the desired frequency domain and time interval between systolic and diastolic phases of the PPG signal.
In vivo image-guided multi-pipette patch-clamp is essential for studying cellular interactions and network dynamics in neuroscience. However, current procedures mainly rely on manual expertise, which limits accessibility and scalability. Robotic automation presents a promising solution, but achieving precise real-time detection of multiple pipettes remains a challenge. Existing methods focus on ex vivo experiments or single pipette use, making them inadequate for in vivo multi-pipette scenarios. To address these challenges, we propose a heatmap-augmented coarse-to-fine learning technique to facilitate multi-pipette real-time localisation for robot-assisted in vivo patch-clamp. More specifically, we introduce a Generative Adversarial Network (GAN)-based module to remove background noise and enhance pipette visibility. We then introduce a two-stage Transformer model that starts with predicting the coarse heatmap of the pipette tips, followed by the fine-grained coordination regression module for precise tip localisation. To ensure robust training, we use the Hungarian algorithm for optimal matching between the predicted and actual locations of tips. Experimental results demonstrate that our method achieved > 98% accuracy within 10 {\mu}m, and > 89% accuracy within 5 {\mu}m for the localisation of multi-pipette tips. The average MSE is 2.52 {\mu}m.
Integrated sensing and communication (ISAC) in the terahertz (THz) band enables obstacle detection, which in turn facilitates efficient beam management to mitigate THz signal blockage. Simultaneously, a THz radio map, which captures signal propagation characteristics through the distribution of received signal strength (RSS), is well-suited for sensing, as it inherently contains obstacle-related information and reflects the unique properties of the THz channel. This means that communication-assisted sensing in ISAC can be effectively achieved using a THz radio map. However, constructing a radio map presents significant challenges due to the sparse deployment of THz sensors and their limited ability to accurately measure the RSS distribution, which directly affects obstacle sensing. In this paper, we formulate an integrated problem for the first time, leveraging the mutual enhancement between sensed obstacles and the constructed THz radio maps. To address this challenge while improving generalization, we propose an integration framework based on a conditional generative adversarial network (CGAN), which uncovers the manifold structure of THz radio maps embedded with obstacle information. Furthermore, recognizing the shared environmental semantics across THz radio maps from different beam directions, we introduce a novel voting-based sensing scheme, where obstacles are detected by aggregating votes from THz radio maps generated by the CGAN. Simulation results demonstrate that the proposed framework outperforms non-integrated baselines in both radio map construction and obstacle sensing, achieving up to 44.3% and 90.6% reductions in mean squared error (MSE), respectively, in a real-world scenario. These results validate the effectiveness of the proposed voting-based scheme.
Transportation planning plays a critical role in shaping urban development, economic mobility, and infrastructure sustainability. However, traditional planning methods often struggle to accurately predict long-term urban growth and transportation demands. This may sometimes result in infrastructure demolition to make room for current transportation planning demands. This study integrates a Temporal Fusion Transformer to predict travel patterns from demographic data with a Generative Adversarial Network to predict future urban settings through satellite imagery. The framework achieved a 0.76 R-square score in travel behavior prediction and generated high-fidelity satellite images with a Structural Similarity Index of 0.81. The results demonstrate that integrating predictive analytics and spatial visualization can significantly improve the decision-making process, fostering more sustainable and efficient urban development. This research highlights the importance of data-driven methodologies in modern transportation planning and presents a step toward optimizing infrastructure placement, capacity, and long-term viability.
Federated Learning (FL) enables collaborative model training across decentralized devices without sharing raw data, but it remains vulnerable to poisoning attacks that compromise model integrity. Existing defenses often rely on external datasets or predefined heuristics (e.g. number of malicious clients), limiting their effectiveness and scalability. To address these limitations, we propose a privacy-preserving defense framework that leverages a Conditional Generative Adversarial Network (cGAN) to generate synthetic data at the server for authenticating client updates, eliminating the need for external datasets. Our framework is scalable, adaptive, and seamlessly integrates into FL workflows. Extensive experiments on benchmark datasets demonstrate its robust performance against a variety of poisoning attacks, achieving high True Positive Rate (TPR) and True Negative Rate (TNR) of malicious and benign clients, respectively, while maintaining model accuracy. The proposed framework offers a practical and effective solution for securing federated learning systems.
Imbalanced data represent a distribution with more frequencies of one class (majority) than the other (minority). This phenomenon occurs across various domains, such as security, medical care and human activity. In imbalanced learning, classification algorithms are typically inclined to classify the majority class accurately, resulting in artificially high accuracy rates. As a result, many minority samples are mistakenly labelled as majority-class instances, resulting in a bias that benefits the majority class. This study presents a framework based on boundary anchor samples to tackle the imbalance learning challenge. First, we select and use anchor samples to train a multilayer perceptron (MLP) classifier, which acts as a prior knowledge model and aids the adversarial and contrastive learning procedures. Then, we designed a novel deep generative model called Anchor Stabilized Conditional Generative Adversarial Network or Anch-SCGAN in short. Anch-SCGAN is supported with two generators for the minority and majority classes and a discriminator incorporating additional class-specific information from the pre-trained feature extractor MLP. In addition, we facilitate the generator's training procedure in two ways. First, we define a new generator loss function based on reprocessed anchor samples and contrastive learning. Second, we apply a scoring strategy to stabilize the adversarial training part in generators. We train Anch-SCGAN and further finetune it with anchor samples to improve the precision of the generated samples. Our experiments on 16 real-world imbalanced datasets illustrate that Anch-SCGAN outperforms the renowned methods in imbalanced learning.