Adversarial contrastive learning aims to learn robust representations from unlabeled data by integrating adversarial training and contrastive learning. Accordingly, existing methods typically generate adversarial perturbations that maximize the contrastive loss during adversarial training. However, these approaches frequently produce ineffective perturbations, as their effectiveness heavily depends on the semantic similarity among samples within each mini-batch, which is not explicitly controlled. As a result, the improvement in robustness remains limited. To address this, we propose a novel approach that leverages the well-structured representation space learned via contrastive learning, where semantically similar samples cluster well while dissimilar ones are positioned farther apart. Exploiting this clustering structure, we construct adversarial perturbations that move samples away from a group of similar samples and toward a group of dissimilar ones, thereby inducing stronger adversarial effects. Compared to existing approaches, our method achieves significant improvements in robust accuracy by up to 4.75% against the PGD attack and 7.59% against Auto-Attack.

The rapid proliferation of software vulnerabilities has created an urgent need for intelligent, automated methods to detect and mitigate security flaws at scale. Traditional vulnerability analysis depends heavily on manual inspection and domain-specific expertise, which are increasingly inadequate in the era of generative AI-driven code development. This research proposes an AI-based automated vulnerability detection and secure code generation framework that leverages multi-modal datasets, including source code and binaries, to achieve end-to- end automation across the vulnerability lifecycle: detection, patch generation, and validation. The system integrates explainable AI (XAI)-based vulnerability cause analysis, generative patch synthesis, system-level defensive code generation, Rust-based memory safety transformation, and differential privacy mechanisms for model confidentiality. Developed through a Korea- U.S. joint research initiative, this project aims to establish an internationally deployable platform for trustworthy and privacy- preserving AI -driven software security. The proposed research contributes both foundational methods and operational tools toward self-healing, explainable, and secure-by-design software ecosystems.

In predictive maintenance (PdM), time-series anomaly detection using IoT sensors has become a common approach for monitoring the condition of industrial machines in real-time. However, it is difficult to determine the condition of the machine accurately due to complex system infrastructure and the insufficient number of anomalies obtained for the training data. In particular, class imbalance derived from a very small set of anomalies leads to the degradation of the anomaly detection model’s performance. To resolve class imbalance, we propose data augmentation based on additive perturbation to improve anomaly detection model performance. Adding generated abnormal data to the training set enhances the model’s ability to capture anomalies based on similarity with noise perturbation. Case studies of industrial air compressors and rectifiers in the rotary machine were conducted to evaluate the proposed perturbation-driven augmentation method. The results showed that the proposed augmentation method could achieve anomaly detection accuracy over 30% higher than recent data augmentation techniques. Thus, we believe that the proposed method can enhance the performance of time-series anomaly detection in predictive maintenance.

Universal application of AI has increased the need to explain why an AI model makes a specific decision in a human-understandable form. Among many related works, the class activation map (CAM)-based methods have been successful recently, creating input attribution based on the weighted sum of activation maps in convolutional neural networks. However, existing methods use channel-wise importance weights with specific architectural assumptions, relying on arbitrarily chosen attribution threshold values in their quality assessment: we think these can degrade the quality of attribution. In this paper, we propose Libra-CAM, a new CAM-style attribution method based on the best linear approximation of the layer (as a function) between the penultimate activation and the target-class score output. From the approximation, we derive the base formula of Libra-CAM, which is applied with multiple reference activations from a pre-built library. We construct Libra-CAM by averaging these base attribution maps, taking a threshold calibration procedure to optimize its attribution quality. Our experiments show that Libra-CAM can be computed in a reasonable time and is superior to the existing attribution methods in quantitative and qualitative attribution quality evaluations.

Model stealing techniques allow adversaries to create attack models that mimic the functionality of black-box machine learning models, querying only class membership or probability outcomes. Recently, interpretable AI is getting increasing attention, to enhance our understanding of AI models, provide additional information for diagnoses, or satisfy legal requirements. However, it has been recently reported that providing such additional information can make AI models more vulnerable to model stealing attacks. In this paper, we propose DeepDefense, the first defense mechanism that protects an AI model against model stealing attackers exploiting both class probabilities and interpretations. DeepDefense uses a misdirection model to hide the critical information of the original model against model stealing attacks, with minimal degradation on both the class probability and the interpretability of prediction output. DeepDefense is highly applicable for any model stealing scenario since it makes minimal assumptions about the model stealing adversary. In our experiments, DeepDefense shows significantly higher defense performance than the existing state-of-the-art defenses on various datasets and interpreters.

In this paper we propose a primal-dual proximal extragradient algorithm to solve the generalized Dantzig selector (GDS) estimation problem, based on a new convex-concave saddle-point (SP) reformulation. Our new formulation makes it possible to adopt recent developments in saddle-point optimization, to achieve the optimal O(1/k) rate of convergence. Compared to the optimal non-SP algorithms, ours do not require specification of sensitive parameters that affect algorithm performance or solution quality. We also provide a new analysis showing a possibility of local acceleration to achieve the rate of O(1/k^2) in special cases even without strong convexity or strong smoothness. As an application, we propose a GDS equipped with the ordered \ell_1-norm, showing its false discovery rate control properties in variable selection. Algorithm performance is compared between ours and other alternatives, including the linearized ADMM, Nesterov’s smoothing, Nemirovski’s mirror-prox, and the accelerated hybrid proximal extragradient techniques.

Abstract: Recent developments in sensor technology allows for capturing dynamic patterns in vehicle movements, tem-perature changes, and sea-level fluctuations, just to name a few. A usual way for decision making on sensor networks, such as detecting exceptional surface level changes across the Pacific ocean, involves collecting measurement data from all sensors to build a predictor in a central processing station. However, data col-lection becomes challenging when communication bandwidth is limited, due to communication distance or low-energy requirements. Also, such settings will introduce unfavorable latency for making predictions on unseen events. In this paper, we propose an alternative strategy for such scenarios, aiming to build a consensus support vector machine (SVM) in each sensor station by exchanging a small amount of sampled information from local kernel matrices amongst peers. Our method is based on decomposing a “global ” kernel defined with all features into “local ” kernels defined only with attributes stored in each sensor station, sampling few entries of the decomposed kernel matrices that belong to other stations, and filling in unsampled entries in kernel matrices by matrix completion. Experiments on benchmark data sets illustrate that a consensus SVM can be built in each station using limited communication, which is competent in prediction performance to an SVM built with accessing all features. 1

Machine learning on resource constrained ubiquitous devices suffers from high energy consumption and slow execution time. In this paper, it is investigated how to modify machine learning algorithms in order to reduce the number of consumed clock cyclesnot by reducing the asymptotic complexity, but by assuming a weaker execution platform. In particular, an integer approximation to the class of undirected graphical models is proposed. Algorithms for inference, maximum-a-posteriori prediction and parameter estimation are presented and approximation error is discussed. In numerical evaluations on synthetic data, the response of the model to several influential properties of the data is investigated. The results on the synthetic data are confirmed with a natural language processing task on an open data set. In addition, the runtime on low-end hardware is regarded. The overall speedup of the new algorithms is at least 2× while overall loss in accuracy is rather small. This allows running probabilistic methods on very small devices, even if they do not contain a processor that is capable of executing floating point arithmetic at all.