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Kirill Lukianov, Mikhail Drobyshevskiy, Georgii Sazonov, Mikhail Soloviov, Ilya Makarov

The rising demand for Trusted AI (TAI) underscores the need for interpretable and robust models, yet existing tools rarely support graph-structured data or integrate interpretability with security. At the same time, Graph Neural Networks (GNNs) deliver state-of-the-art performance on numerous graph tasks. We present GNN-AID (Graph Neural Network Analysis, Interpretation, and Defense), an open-source Python framework for analyzing, interpreting, and defending GNNs, addressing this critical gap. Built on PyTorch-Geometric, GNN-AID offers preloaded datasets, model libraries, flexible APIs, and a web interface for visualization and no-code model design. MLOps features further support reproducibility and experiment tracking.

Xinyu Li

The widespread adoption of artificial intelligence (AI) in cybersecurity has led to the emerging threat of AI-driven cyberattacks, such as LLM-empowered Advanced Persistent Threats (APTs), challenging the effect of conventional deception defense mechanisms. To fill this critical gap, my work aims to develop a game-theoretic defense AI agent capable of providing the optimal deception resource deployment strategy, to establish AI-driven defenses against AI-empowered cyberattacks. In this proposal, I model the attacker and defender interaction as a dynamic game with incomplete information between AI agents, and then derive the equilibrium defense strategies. Synthetic data based experiments and real-world implementations would be conducted to validate the proposed framework. This study has the potential to improve the effectiveness of deception defense in three dimensions: scalability, real-time capability, and strategic intelligence.

Woo Jon Hou Ainsley

Generative AI shows strong capabilities in language, reasoning, and code but remains prone to hallucinations—outputs that are fluent yet incorrect. In cybersecurity, such errors pose serious risks, from misleading analysts to potential adversarial exploitation. This project investigates hallucinations in three directions: (1) creating benchmarks and interpretability tools to characterize them in security contexts; (2) developing mitigation strategies such as retrieval-augmented generation, symbolic-neural hybrids, and uncertainty-aware decoding; and (3) integrating these methods into real-world workflows like vulnerability assessment, malware analysis, and penetration testing, while exploring how attackers might exploit hallucinations. Evaluation will combine accuracy metrics, human-in-the-loop studies, and red-team simulations. By bridging theory and applied system design, the work aims to advance understanding of hallucinations and improve the reliability of AI in cybersecurity, with broader implications for other high-stakes areas such as healthcare and law.

Tongle Zhao, Fang Fan, Huiqi Zhao, Xiaodu Liu

Encrypted traffic classification has become increasingly important in network security. To address the difficulty of existing architectures in collaboratively modeling spatio-temporal features, we propose BiST-Mamba, a novel dual-branch spatio-temporal Mamba network that enables simultaneous representation of spatio-temporal features. To the best of our knowledge, this is the first work to introduce VMamba into encrypted traffic classification. Preliminary experiments on a small-scale dataset show that our accuracy and F1 scores reach 94.13% and 93.41%, respectively. The method achieves promising classification performance, demonstrating the potential of the model for effective spatio-temporal modeling.

Suchetan G. Uppur, Ashish Kumar, Akshay Agarwal

The Completely Automated Public Turing test to Tell Computers and Humans Apart (CAPTCHA) is widely deployed on the web as a security mechanism to distinguish humans from automated bots. However, their robustness is being challenged by the rapid advancements in AI, with models capable of near-human level character recognition rendering CAPTCHA obsolete. This research aims to systematically study the effect of multiple image corruptions, including elastic transformations, blur, noise, and occlusions, on human readability and automated solvers in text-based CAPTCHA recognition. We conduct experiments on multimodal large language models (MLLMs), a traditional deep learning-based optical character recognition (OCR) system, and human subjects. Using an existing CAPTCHA dataset and artificially corrupted versions, we analyze the recognition performance of AI models and humans, identifying vulnerabilities and patterns of robustness. The findings will contribute to a better understanding of CAPTCHA vulnerabilities and explore potential methods to increase the robustness of CAPTCHA in the era of advanced AI models.

Malak Mahdy, Carlos Rubio-Medrano

Autonomous computer-use agents (ACUAs) enable end-to-end computer operation with human-like capabilities, executing commands across applications and making independent decisions. However, their real-world effectiveness and security remain largely untested. A systematic evaluation of ACUAs from Anthropic, OpenAI, and open-source projects categorized them into full computer access and browser-based agents. Findings reveal substantial limitations, with success rates dropping as low as 28% in some cases. Additionally, a 100% rate of unauthorized software installation was observed in certain tasks. The agents also demonstrated susceptibility to prompt injection attacks. The impact of varied prompting strategies on performance was also examined. In response to these weaknesses, a new agent framework designed to address these limitations is proposed. This work bridges agentic AI, human-computer interaction (HCI), and security to address the observed limitations of ACUAs, prioritizing both capability and safety.

Ungsik Kim, Minjae Lee, Jiho Bae, Minje Kim, Sang-Min Choi, Suwon Lee

We propose a framework for privacy-preserving argumentative explanations using homomorphic encryption. This method applies the Cheon-Kim-Kim-Song scheme, along with a soft k-means adapted for encrypted computation, to generate explanations without exposing sensitive data. By leveraging GPU acceleration, speedups of approximately 470–670 times were achieved compared with CPU execution. Experimental results show that explanation fidelity is maintained for small- to medium-scale models, whereas significant degradation occurs in larger models. These findings suggest that our study provides an initial step toward enabling secure and trustworthy argumentative explanations under encryption while also highlighting the challenges that remain for generalizability to more complex models.

Aravinthakshan A S, Aditya Prashant Naidu, Aadiv Rath

Camouflaged object detection is critical for military, defense, and security operations, where targets evade conventional surveillance by mimicking the background or exhibiting low-contrast differences. It also supports non-invasive monitoring of elusive wildlife and endangered species, improving population estimates, habitat management, and biodiversity assessments by recovering objects that are visually indistinguishable from their surroundings. Existing solutions are computationally heavy, with large model parameters and high computational demands, which hinder deployment in real-world applications. Lightweight models have been explored, but they often compromise fine boundary fidelity. This paper introduces a lightweight Laplacian pyramid–based feature extractor that progressively aggregates multiscale Laplacian features with frequency information. The proposed architecture emphasizes object edge boundaries, enabling precise localization under subtle target–background differences while maintaining realtime efficiency. The design achieves performance comparable to the state of the art (SOTA) convolution based methods on CHAMELEON and NC4K datasets.

Thi Kieu Khanh Ho

Time-series data, which represent the evolution of one or more variables over time, are ubiquitous across domains such as finance, medicine, industry, and security. Time-Series Anomaly Detection (TSAD) is essential for identifying irregular events such as equipment failures, fraudulent activities, and neurological disorders. Despite significant progress, TSAD remains challenging due to the complexity of time-series signals, the diversity of anomaly types, and the scarcity of high-quality labeled data. This thesis contributes: (i) the first comprehensive surveys of Graph-based TSAD (G-TSAD) and Self-Supervised Learning for Anomaly Detection (SSL-AD), showing how graph modeling and SSL proxy tasks yield robust representations for TSAD while mapping limits and future directions; (ii) EEG-CGS, a contrastive–generative SSL framework that encodes fine-grained subgraph structure without anomaly labels, improving multivariate TSAD and localizing anomalous sensors and regions; (iii) TSAD-C, which integrates graph representations with diffusion models to capture long-range temporal and spatial dependencies while explicitly handling contaminated training data; and (iv) extending TSAD beyond benchmark datasets into other impactful domains, and developing foundation models specialized for biosignals to detect novel anomalies in drug-resistant epilepsy patients.

Tanusree Ghosh

The rise of generative AI presents a profound duality. On one hand, it offers a powerful solution to data scarcity and privacy challenges in biometrics. On the other, it is weaponized to create deepfakes that threaten digital integrity. Existing detectors for these deepfakes are brittle, failing against real-world transformations and novel generative models. This dissertation confronts this duality head-on. First, I establish the viability of synthetic data for building fair and private biometric systems. Second, to counter the malicious use of this technology, this dissertation develops deepfake detectors designed to be robust, generalizable, and efficient by construction. My work introduces novel, lightweight feature sets on different cues (e.g., colour cue-based Relative Chrominance Difference, Gradient features, Depth cues, etc.) that are inherently resilient to OSN transformations and improve generalisation to unseen forgeries. Whereas, accomplished results confirm state-of-the-art performance, achieving high accuracy in challenging real-world scenarios with a significant reduction in model complexity, my current and future work focuses on achieving superior generalisation while being OSN manipulation resistant.

Mehdi Yekrangi, Houssem Chatbri, Claudia Beatrice Chianella, Owen O'Neill

The increasing adoption of large language models in the fi-nancial sector introduces significant challenges related to the handling of sensitive financial information (SFI). Existing general-purpose content safety solutions, or guardrails, often fall short in detecting domain-specific risks inherent in finan-cial data processing. This study addresses these gaps by de-veloping a comprehensive taxonomy of SFI, grounded in globally recognized financial, information security, and AI governance standards. Leveraging this taxonomy, we synthe-sized an extensive dataset encompassing diverse categories of SFI and trained GARD (Generative Adversarial network Risk Detection) model to detect sensitive content in both in-puts and outputs of GenAI systems within the financial do-main. Our evaluation compared GARD against commercial guardrail solutions, including the OpenAI Moderation API and Microsoft Azure Content Safety (ACS). The results demonstrated that while commercial solutions maintained high precision, their recall was substantially lower, indicating many risky instances went undetected. In contrast, our model achieved a recall score of 0.98, significantly outperforming the benchmarks and enhancing SFI detection. These findings underscore the necessity of domain-specific guardrails tai-lored to the financial sector to ensure robust AI safety and compliance. In conclusion, this work contributes (1) A de-tailed taxonomy of SFI tailored for GenAI applications, (2) A comprehensive synthetic dataset that encompasses a wide range of sensitive topics relevant to the domain and (3) A high-performance risk detection model that can be deployed independently or alongside existing solutions to improve con-tent safety in financial services. This approach promotes trust, mitigates financial, legal, and reputational risks, and supports the responsible adoption of GenAI technologies in sensitive domains.

Youngkyu Lee, Jinho Lee, Youngdae Jo, Jeongwoo Park

Timely detection of retinal diseases is crucial for preventing vision loss; yet the limited availability of ophthalmologists and disparities in access to diagnostic services continue to hinder widespread screening, particularly in primary care settings. We present REMEDIS, a Software-as-a-Service (SaaS)-based clinical AI framework for the automated diagnosis of major retinal diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), epiretinal membrane (ERM), and glaucoma, using fundus images. The system analyzes high-resolution fundus photographs in a secure cloud environment via a Swin-Large-based multi-disease classification network, producing disease-specific probability scores. To ensure clinically meaningful decision making, Youden’s Index is applied to determine optimized sensitivity-specificity thresholds for each condition. An explainability module based on Grad-CAM generates lesion localization contour visualizations, providing interpretable evidence that assists ophthalmologists in case review and facilitates integration into electronic medical records (EMR). The framework was evaluated in an IRB-approved multicenter prospective clinical trial conducted under real-world conditions, achieving an average AUC exceeding 0.94 across the four target diseases and demonstrating strong concordance with expert diagnoses. To our knowledge, this represents one of the first SaaS-based AI diagnostic frameworks for retinal diseases validated through prospective clinical studies, highlighting its potential as an emerging clinical application of AI.

Tarun Kumar, Aalap Tripathy, Gayathri Saranathan, Martin Foltin, Suparna Bhattacharya, Scott Hinchley, Donald M Bahls, David Brookshire, Larry Kaplan, Robert W. Wisniewski

The proliferation of Model Context Protocol (MCP) servers in enterprise infrastructure management has revolutionized AI-driven automation while introducing critical multi-layered security vulnerabilities that traditional cybersecurity frameworks cannot adequately address. This paper presents a comprehensive intelligent guardrail system that addresses the unique security challenges of MCP-driven infrastructure management through a novel four-layer defense architecture. Our solution employs a dedicated guardian LLM that interprets natural language policies and applies contextual reasoning to complex infrastructure scenarios, providing dynamic policy enforcement that adapts to user roles, operational timing, and system context. Unlike existing rule-based security systems, our approach implements guardrails at four distinct control points: input message filtering, tool selection validation, execution-time verification, and post-action auditing. The system addresses critical gaps in existing security solutions by providing infrastructure-specific threat modeling, real-time policy adaptation, and comprehensive audit trails with explainable decision-making through confidence scores and detailed reasoning. Our evaluation demonstrates the system's effectiveness in preventing command injection, privilege escalation, and tool poisoning attacks across various enterprise infrastructure scenarios while maintaining operational agility essential for modern data center management.

Qiming Guo, Bishal Khatri, Wenbo Sun, Jinwen Tang, Hua Zhang, Wenlu Wang

Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.

Naveen Janaki Raman, Jingwu Tang, Zhiyu Chen, Zheyuan Ryan Shi, Sean Hudson, Ameesh Kapoor, Fei Fang

Food rescue organizations simultaneously tackle food insecurity and waste by working with volunteers to redistribute food from donors who have excess to recipients who need it. Volunteer feedback allows food rescue organizations to identify issues early and ensure volunteer satisfaction. However, food rescue organizations monitor feedback manually, which can be cumbersome and labor-intensive, making it difficult to prioritize which issues are most important. In this work, we investigate how large language models (LLMs) assist food rescue organizers in understanding and taking action based on volunteer experiences. We work with 412 Food Rescue, a large food rescue organization based in Pittsburgh, Pennsylvania, to design RescueLens, an LLM-powered tool that automatically categorizes volunteer feedback, suggests donors and recipients to follow up with, and updates volunteer directions based on feedback. We evaluate the performance of RescueLens on an annotated dataset, and show that it can recover 96% of volunteer issues at 71% precision. Moreover, by ranking donors and recipients according to their rates of volunteer issues, RescueLens allows organizers to focus on 0.5% of donors responsible for more than 30% of volunteer issues. RescueLens is now deployed at 412 Food Rescue and through semi-structured interviews with organizers, we find that RescueLens streamlines the feedback process so organizers better allocate their time.

Mayank Kejriwal

Online human trafficking investigations generate vast amounts of noisy, heterogeneous, and deliberately obfuscated data, making traditional search and analytics tools ineffective for supporting law enforcement. This paper discusses the deployment of the Domain-Specific Insight Graphs (DIG) system, an AI-powered investigative search engine that was operationally used by over 200 U.S. law enforcement agencies for more than five years in the pre-COVID period. The system integrates advanced research conducted over the years in information extraction, knowledge graph construction, and entity-centric search to enable investigators to formulate queries without technical background, aggregate evidence, and uncover latent relationships among entities such as phone numbers, emails, and locations. Beyond technical innovation, the deployment required sustained attention to usability, explainability, and policy compliance, ensuring trust in high-stakes legal contexts. We report measurable benefits in investigative efficiency, case initiation, and prosecutorial support, as well as lessons learned from long-term maintenance and adaptation to evolving online platforms. Since 2020, work conducted in this domain has also had significant policy and advocacy ramifications. The system's generalized design has also allowed it to be prototyped for adjacent illicit domains, including securities fraud and illegal firearm sales, demonstrating the broader applicability of AI-driven investigative tools. We contribute a rare case study of an AI system that has transitioned from research to sustained real-world impact in a socially critical domain.

Min Ren, Yunlong Wang, Yuhao Zhu, Yongzhen Huang, Zhenan Sun, Qi Li, Tieniu Tan

Deep learning-based face recognition models are vulnerable to adversarial attacks. In contrast to general noises, the presence of imperceptible adversarial noises can lead to catastrophic errors in deep face recognition models. The primary difference between adversarial noise and general noise lies in its specificity. Adversarial attack methods give rise to noises tailored to the characteristics of the individual image and recognition model at hand. Diverse samples and recognition models can engender specific adversarial noise patterns, which pose significant challenges for adversarial defense. Addressing this challenge in the realm of face recognition presents a more formidable endeavor due to the inherent nature of face recognition as an open set task. In order to tackle this challenge, it is imperative to employ customized processing for each individual input sample. Drawing inspiration from the biological immune system, which can identify and respond to various threats, this paper aims to create an artificial immune system to provide adversarial defense for face recognition. The proposed defense model incorporates the principles of antibody cloning, mutation, selection, and memory mechanisms to generate a distinct antibody for each input sample, wherein the term antibody refers to a specialized noise removal manner. Furthermore, we introduce a self-supervised adversarial training mechanism that serves as a simulated rehearsal of immune system invasions. Extensive experimental results demonstrate the efficacy of the proposed method, surpassing state-of-the-art adversarial defense methods.

Xuefeng Du

This talk surveys my research journey toward building reliable machine learning systems that behave safely and predictably in the open world. While modern machine learning models—including foundation models (FMs)—have demonstrated unprecedented capabilities, they often suffer from reliability failures under distribution shift, leading to overconfident mispredictions, hallucinated generations, or susceptibility to adversarial prompts. My research rethinks reliability not as an afterthought, but as a first-class algorithmic principle, to be optimized alongside accuracy with minimal human supervision. The talk is organized around three key threads. To respect the allotted 20-30 minutes, the first and second parts will be briefly discussed. 1. Unknown-Aware Learning via Outlier Synthesis. I introduce a class of learning algorithms that synthesize “virtual outliers” in representation or pixel space to explicitly teach models what they don’t know. This includes the VOS, NPOS, and Dream-OOD frameworks, which shape the energy landscape around in-distribution data to avoid overconfidence on OOD. 2. Learning in the Wild with Unlabeled Data. I present theoretical insights and practical algorithms for leveraging unlabeled in-the-wild data to improve reliability. This includes SAL framework, which uses a gradient-based spectral method to separate potential outliers, and SCONE, which handles semantic and covariate shifts via constrained optimization. These results turn unlabeled data contamination into a learning signal. 3. Reliable Foundation Models. I explore reliability failures in LLMs and multimodal systems. I introduce HaloScope for hallucination detection via subspace separation on LLM representations, and TSV that performs LLM latent steering for improved hallucination detection. I will also briefly cover the LLM security and alignment, which includes VLMGuard for detecting malicious prompts in vision-language models and a data-centric paradigm for AI alignment through source-aware feedback cleaning. Throughout the talk, I highlight how representation learning, data generation, and theoretical guarantees intersect to produce scalable, label-efficient reliability methods. I will also reflect on my broader vision: designing proactive and collaborative AI systems that anticipate uncertainty and support rich human-AI interaction—especially for underrepresented communities and emerging scientific domains. This talk will be accessible to a broad AAAI audience, combining foundational algorithmic insights with real-world applications and forward-looking perspectives on the future of responsible AI.

Adib Hasan, Mardavij Roozbehani, Munther A. Dahleh

Accurate crop yield forecasting is essential for global food security. However, current AI models systematically underperform when yields deviate from historical trends. We attribute this to the lack of rich, physically grounded datasets directly linking atmospheric states to yields. To address this, we introduce VITA (Variational Inference Transformer for Asymmetric Data), a variational pretraining framework that learns representations from large satellite-based weather datasets and transfers to the ground-based limited measurements available for yield prediction. VITA is trained using detailed meteorological variables as proxy targets during pretraining and learns to predict latent atmospheric states under a seasonality-aware sinusoidal prior. This allows the model to be fine-tuned using limited weather statistics during deployment. Applied to 763 counties in the US Corn Belt, VITA achieves state-of-the-art performance in predicting corn and soybean yields across all evaluation scenarios, particularly during extreme years, with statistically significant improvements (paired t-test, p < 0.0001). Importantly, VITA outperforms prior frameworks like GNN-RNN without soil data, and larger foundational models (e.g., Chronos-Bolt) with less compute, making it practical for real-world use, especially in data-scarce regions. This work highlights how domain-aware AI design can overcome data limitations and support resilient agricultural forecasting in a changing climate.

Wenchao Dong, Marcelo Sartori Locatelli, Virgilio Almeida, Meeyoung Cha

Climate change poses a global threat to public health, food security, and economic stability. Addressing it requires evidence-based policies and a nuanced understanding of how the threat is perceived by the public, particularly within visual social media, where narratives quickly evolve through voices of individuals, politicians, NGOs, and institutions. This study investigates climate-related discourse on YouTube within the Brazilian context, a geopolitically significant nation in global environmental negotiations. Through three case studies, we examine (1) which psychological content traits most effectively drive audience engagement, (2) the extent to which these traits influence content popularity, and (3) whether such insights can inform the design of persuasive synthetic campaigns such as climate denialism using recent generative language models. Another contribution of this work is the release of a large publicly available dataset of 226K Brazilian YouTube videos and 2.7M user comments on climate change. The dataset includes fine-grained annotations of persuasive strategies, theory of mind categorizations in user responses, and typologies of content creators. This resource can help support future research on digital climate communication and the ethical risk of algorithmically amplified narratives and generative media.