Securing micro-expressions against leakage is crucial for privacy, as these subtle facial movements convey genuine emotions and are inherently personal. This study aims to protect micro-expression data from potential adversarial attacks, ensuring the preservation of individuals’ privacy and preventing unauthorized access or misuse of sensitive emotional information. Unlike traditional methods, which often require training and extensive access to models, this research introduces a novel post-hoc method that does not require additional training. We focus on physical adversarial attacks in micro-expression recognition, involving intentional manipulation of visual cues to deceive recognition systems and protect individual emotional privacy. Our approach leverages a causal discovery algorithm to identify causal relationships between facial parts, enabling rapid identification of the optimal locations for adversarial patches in frames with triggered micro-expressions. This method exhibits a more consistent attack success rate than randomly placed adversarial stickers, demonstrating effective generalization across different emotions, stickers, and models. Particularly relevant in scenarios with restricted access to the model, our technique requires only a single interaction during the attack process, highlighting its efficiency and minimal need for querying the target model. The proposed method effectively balances privacy protection with high generalization capability, setting a new standard for defending against adversarial threats in micro-expression recognition. The code is available at https://github.com/noobasuna/au-sticker.

Microscopic visualization of diseased cells plays a vital role in the diagnosis and understanding of various medical conditions. Recent advances in deep learning generative models have shown remarkable potential as formidable tools for generating high-quality medical images. However, training these models generally requires large, annotated datasets, which are often costly and time-consuming to obtain. To overcome this challenge, we propose a fast-sampling guided score-based diffusion model with a classifier-free guidance strategy for class-conditioned generation of microscopic peripheral blood cell images. Our model achieves a Fréchet Inception Distance (FID) score of 10.24, demonstrating its ability to generate realistic synthetic blood cell images. Furthermore, our experimental results show that augmenting training data with these synthetic images significantly improves classification accuracy compared to relying solely on real data, highlighting the potential of synthetic data augmentation in hematology.

Brain functional connectivity (FC) changes are associated with neuropsychiatric disorders and other underlying factors, such as age and gender. Due to small training sample, data augmentation has been increasingly used for deep learning-based classification of brain FC. Although deep generative models could generate brain FCs to enhance downstream classification, most existing methods neglect the underlying factors involved in the generation process and fail to preserve the subject identity. We propose a novel brain FC conditional Generative Adversarial Network (GAN) called BrainFC-CGAN with specialized layers and filters to preserve the symmetry property and topological structure of brain FCs. We design a FC generator that captures the complex variations between brain FCs, ages, and health statuses to generate synthetic FCs that preserve the subject identity. We categorized true brain FCs into different age groups; an augmented age-specific dataset generated from BrainFC-CGAN is combined with the training set for classification. Experimental results on major depressive disorder (MDD) resting-state functional magnetic resonance imaging data show that the proposed method synthesizes realistic brain FCs of different target age groups, significantly improving downstream classification performance over baseline without augmentation, and also outperforming several state-of-the-art GANs.

Analyzing the community structure of brain networks provides new insights into human brain function. Existing studies broadly use conventional network clustering approaches. While graph neural networks have recently shown promise in modeling brain functional connectivity (FC) networks, their applications to brain community detection still need improvement and further refinement. Moreover, identifying common community structure while resolving the single-subject partitions across multiple individual networks remains underexplored. We propose a Deep Multi-Graph Embedded Clustering (DMGEC) framework to identify shared community partition in brain FC networks over a cohort of individuals. By incorporating the consensus information aggregated across network structures, DMGEC leverages a graph autoencoder to produce consensus-aware latent representations of individual networks, and applies deep embedded clustering on the multi-subject network representation to produce common community assignment of brain nodes. Simulations show superior community recovery by our method compared to conventional approaches, especially for networks with large number of communities. When applied to functional magnetic resonance imaging (fMRI) data, the DMGEC achieves outstanding alikeness over individual partitions, and uncovers group-level differences in brain community motifs between major depressive disorder patients and normal controls.

Deep learning (DL) methods recently show promise on accurate brain disorder classification using functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI). However, DL model building can be hindered by small sample-size settings of fMRI. Moreover, most studies utilize either static (sFC) or dynamic FC (dFC) for classification. We propose a unified framework for data augmentation of both sFC and dFC for multi-domain joint classification of brain disorders. We exploit generative adversarial networks (GAN) to synthesize realistic FCs for data augmentation. Notably, we adopted the TimeGAN for dFC generation that can capture temporal dependencies in real dFC, and the GR-SPD-GAN for sFC generation that preserves the spatial connectivity structure. We further develop BrainFusionNet - a specialized DL model for multi-domain FC that simultaneously learns embedded features from both sFC and dFC to provide complementary spatio-temporal information for downstream classification. The synthetic FC data are augmented in training data to improve the BrainFusionNet performance and generalizability. Experimental results on major depressive disorder (MDD) identification using resting-state fMRI show substantial improvement in classification accuracy by our framework, outperforming competing models without FC augmentation and using sFC or dFC features alone.