Comprehensive Evaluation of Deepfake Detection Models: Accuracy, Generalization, and Resilience to Adversarial Attacks by Maryam Abbasi, Paulo Váz, José Silva, Pedro Martins

“…Existing detection methods face challenges with generalization across datasets and vulnerability to adversarial attacks. …”
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Learning Deceptive Strategies in Adversarial Settings: A Two-Player Game with Asymmetric Information by Sai Krishna Reddy Mareddy, Dipankar Maity

“…This work advances the design of intelligent agents capable of strategic reasoning under uncertainty and adversarial conditions.…”
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Fault Recognition Method and Application Based on Generative Adversarial Network by Shuiliang Luo, Yongmei Huang, Yun Su, Shengkui Wang, Qianqian Liu, Yingqiang Qi, Fuhao Chang

“…ABSTRACT In view of the limitation of generalization ability faced by deep learning in fault identification, especially in the case of complex underground geological conditions and variable seismic data characteristics, it is often ineffective to directly use the network based on synthetic data training for fault prediction of real data. …”
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SpaGAN: A spatially-aware generative adversarial network for building generalization in image maps by Zhiyong Zhou, Cheng Fu, Robert Weibel

“…The progress of deep learning offers a new paradigm to overcome the coordination challenges faced by conventional building generalization algorithms. Some studies have confirmed the feasibility of several original semantic segmentation networks, such as U-Net and its variants and the conditional generative adversarial network (cGAN), for building generalization in image maps. …”
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Conditional Generative Adversarial Networks and Deep Learning Data Augmentation: A Multi-Perspective Data-Driven Survey Across Multiple Application Fields and Classification Archit... by Lucas C. Ribas, Wallace Casaca, Ricardo T. Fares

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A highly generalized federated learning algorithm for brain tumor segmentation by Jun Wen, Xiusheng Li, Xin Ye, Xiaoli Li, Hang Mao

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Learning From Imbalanced Data Using Triplet Adversarial Samples by Jaesub Yun, Jong-Seok Lee

“…In addition, we present a model training approach to further improve the generalization of the model to small classes by providing a diverse set of synthetic samples optimized using our proposed loss function. …”
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Overall Layout Method of Frame Structure Plane Based on Generative Adversarial Network by ZHONG Yan, LEI Xin, LONG Danbing, FANG Changjian, KANG Yongjun

“…Once the PF‒structGAN model is trained, the architectural and partial structure feature maps are input into the optimal model to generate a frame structure layout.Results and DiscussionsA total of 5 120 dataset pairs were created for training the generative model—4 320 for training and 800 for testing. The training set was input into the pix2pixHD framework, and training was stopped once adversarial training reached a Nash equilibrium. …”
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Data generation for asphalt pavement evaluation: Deep learning-based insights from generative models by Mohammad Sedighian-Fard, Amir Golroo, Mahdi Javanmardi, Alexandre Alahi, Mehdi Rasti

“…Its generalization capabilities were validated across multiple real-world datasets, demonstrating reliable performance under diverse crack patterns and imaging conditions. …”
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Generative Adversarial Network for Real‐Time Flash Drought Monitoring: A Deep Learning Study by Ehsan Foroumandi, Keyhan Gavahi, Hamid Moradkhani

“…The developed architecture in this study employs a Markovian discriminator, which emphasizes the spatial dependencies, with a modified U‐Net generator, tuned for optimal performance. To determine the best loss function for the generator, four different networks are developed with different loss functions, including Mean Absolute Error (MAE), adversarial loss, a combination of adversarial loss with Mean Square Error (MSE), and a combination of adversarial loss with MAE. …”
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Multi-Channel Speech Enhancement Using Labelled Random Finite Sets and a Neural Beamformer in Cocktail Party Scenario by Jayanta Datta, Ali Dehghan Firoozabadi, David Zabala-Blanco, Francisco R. Castillo-Soria

“…In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. …”
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Enhancing generalization in a Kawasaki Disease prediction model using data augmentation: Cross-validation of patients from two major hospitals in Taiwan. by Chuan-Sheng Hung, Chun-Hung Richard Lin, Jain-Shing Liu, Shi-Huang Chen, Tsung-Chi Hung, Chih-Min Tsai

“…Secondly, we introduce a combined model, the Disease Classifier with CTGAN (CTGAN-DC), which integrates DC with Conditional Tabular Generative Adversarial Network (CTGAN) technology to improve data balance and predictive performance further. …”
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Deep Learning-Based Denoising for Optical Coherence Tomography: Evaluating Self-Supervised and Generative Models Across Retinal Datasets by Diogen BABUC, Alesia LOBONŢ, Alexandru FARCAŞ, Todor IVAŞCU, Sebastian-Aurelian ŞTEFĂNIGĂ

“…We aimed to evaluate the performance of five deep learning-based denoising models, namely Zero Shot Noise2Noise (ZS-N2N), DnCNN (for Gaussian denoising), U-Net Autoencoder, SwinIR Transformer, and CycleGAN. …”
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Multi-Scale Feature Fusion GANomaly with Dilated Neighborhood Attention for Oil and Gas Pipeline Sound Anomaly Detection by Yizhuo Zhang, Zhengfeng Sun, Shen Shi, Huiling Yu

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Cross-dataset evaluation of deep learning models for crack classification in structural surfaces by Rashid Taha, Mokji Musa Mohd, Rasheed Mohammed

“…This study investigates how well deep learning models generalize for crack classification across varied datasets and identifies which models perform best under self-testing and cross-testing conditions. …”
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Generative Data Modelling for Diverse Populations in Africa: Insights from South Africa by Sally Sonia Simmons, John Elvis Hagan, Thomas Schack

“…Information missing from SAGE Wave 1, including demographic (e.g., race, age) and health (e.g., hypertension, blood pressure) indicators, were imputed using Generative Adversarial Imputation Nets (GAIN). CopulaGAN, CTGAN, and TVAE, sourced from the sdv 1.24.1 python library, generated 104,227 synthetic records based on the SAGE data constituents. …”
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On the Application of a Sparse Data Observers (SDOs) Outlier Detection Algorithm to Mitigate Poisoning Attacks in UltraWideBand (UWB) Line-of-Sight (LOS)/Non-Line-of-Sight (NLOS) C... by Gianmarco Baldini

“…The proposed techniques are applied to two data sets: the public eWINE data set with seven different UWB LOS/NLOS different environments and a radar data set with the LOS/NLOS condition. …”
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Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising by Samy Abd El-Nabi, Ahmed F. Ibrahim, El-Sayed M. El-Rabaie, Osama F. Hassan, Naglaa F. Soliman, Khalil F. Ramadan, Walid El-Shafai

“…Moreover, a detailed sensitivity analysis of data augmentation strategies reveals that techniques such as rotation and horizontal flip substantially enhance the model’s generalization across variable visual inputs. The system also demonstrates improved robustness under real-world black-box scenarios and adversarial conditions. …”
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Research on Super-Resolution Reconstruction of Coarse Aggregate Particle Images for Earth–Rock Dam Construction Based on Real-ESRGAN by Shuangping Li, Lin Gao, Bin Zhang, Zuqiang Liu, Xin Zhang, Linjie Guan, Junxing Zheng

“…The paper begins with a review of traditional image super-resolution methods, introducing Generative Adversarial Networks (GAN) and Real-ESRGAN, which effectively enhance image detail recovery through perceptual loss and adversarial training. …”
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MA-DenseUNet: A Skin Lesion Segmentation Method Based on Multi-Scale Attention and Bidirectional LSTM by Wenbo Huang, Xudong Cai, Yang Yan, Yufeng Kang

“…Furthermore, it incorporates both channel and spatial attention mechanisms along with temporal modeling to improve boundary delineation and segmentation accuracy. A generative adversarial network (GAN) is also introduced to refine the segmentation output and boost generalization performance. …”
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