While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples t...
This paper presents a novel adversarial defense framework that strategically exploits the non-transferability of adversarial attacks across multi-modal foundation models. While Contrastive Language–Image Pre-training (CLIP) models demonstrate r...
Vision–language models (VLMs), such as Contrastive Language–Image Pretraining (CLIP), are increasingly deployed in real-world applications, including content moderation, misinformation detection, and fraud analysis, making their robustnes...
This study aims to effectively defend against complex and diverse adversarial attacks, enhance adversarial defense performance in the field of textual adversarial examples, and address existing issues in current defense methods such as models’...
Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversari...
In this work, we formalize the concept of differential model robustness (DMR), a new property for ensuring model security in federated learning (FL) systems. For most conventional FL frameworks, all clients receive the same global model. If there exi...
World models have demonstrated powerful environment modeling capabilities in scenarios such as autonomous driving and robotics, but their adversarial security issues remain underexplored, in particular, adversarial risk analysis of world models. To b...
The field of object detection has witnessed significant advancements in recent years, thanks to the remarkable progress in artificial intelligence and deep learning. These breakthroughs have significantly enhanced the accuracy and efficiency of detec...
Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture. However, the critical challenge in utilizing AI models in...
In the field of network intrusion, malware usually evades anomaly detection by disguising malicious behavior as legitimate access. Therefore, detecting these attacks from network traffic has become a challenge in this an adversarial setting. In this...
Physical adversarial attacks face significant challenges in achieving transferability across different object detection models, especially in real-world conditions. This is primarily due to variations in model architectures, training data, and detect...
We address the task of point cloud denoising by leveraging a diffusion-based generative framework augmented with adversarial training. While recent diffusion models have demonstrated strong capabilities in learning complex data distributions, their e...
When dealing with non-IID data, federated learning confronts issues such as client drift and sluggish convergence. Therefore, we propose a Bidirectional Corrective Model-Contrastive Federated Adversarial Training (BCMCFAT) framework. On the client si...
The recommendation model based on the knowledge graph (KG) alleviates the problem of data sparsity in the recommendation to a certain extent and further improves the accuracy, diversity, and interpretability of recommendations. Therefore, the knowled...
Adversarial patch attacks are core tools to evaluate adversarial robustness in remote sensing image classification, yet existing methods with fixed patch shapes and single-model optimization yield poor cross-architecture black-box transferability. Th...
Machine learning models used in financial decision systems create a measurable risk tradeoff between predictive performance and resilience to adversarial manipulation. This study presents a governance framework for managing that tradeoff through step...
Foundation models play an increasingly pivotal role in the field of deep neural networks. Given that deep neural networks are widely used in real-world systems and are generally susceptible to adversarial attacks, securing foundation models becomes a...
Object recognition is a fundamental concept in computer vision. Object detection models have recently played a vital role in various applications, including real-time and safety-critical systems such as camera surveillance and self-driving cars. Scie...
Large visual language models like Contrastive Language-Image Pre-training (CLIP), despite their excellent performance, are highly vulnerable to the influence of adversarial examples. This work investigates the accuracy and robustness of visual langua...
The rise of deepfakes—synthetic media generated using artificial intelligence—threatens digital content authenticity, facilitating misinformation and manipulation. However, deepfakes can also depict real or entirely fictitious individuals...
Deep neural networks (DNNs) have been widely utilized in automatic visual navigation and recognition on modern unmanned aerial vehicles (UAVs), achieving state-of-the-art performances. However, DNN-based visual recognition systems on UAVs show seriou...
As AI becomes indispensable in healthcare, its vulnerability to adversarial attacks demands serious attention. Even minimal changes to the input data can mislead Deep Learning (DL) models, leading to critical errors in diagnosis and endangering patie...
Several attacks have been proposed against autonomous vehicles and their subsystems that are powered by machine learning (ML). Road sign recognition models are especially heavily tested under various adversarial ML attack settings, and they have prov...
Visual object tracking is an important technology in camera-based sensor networks, which has a wide range of practicability in auto-drive systems. A transformer is a deep learning model that adopts the mechanism of self-attention, and it differential...
This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wag...
Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision, in particular. Compared to such examples, however, there have been more limited applications of GAN...
27 November 2023
Deep Learning has enabled significant progress towards more accurate predictions and is increasingly integrated into our everyday lives in real-world applications; this is true especially for Convolutional Neural Networks (CNNs) in the field of image...
The vulnerability of deep-learning-based image classification models to erroneous conclusions in the presence of small perturbations crafted by attackers has prompted attention to the question of the models’ robustness level. However, the quest...
In the rapidly advancing field of Artificial Intelligence (AI), this study presents a critical evaluation of the resilience and cybersecurity efficacy of leading AI models, including ChatGPT-4, Bard, Claude, and Microsoft Copilot. Central to this res...
Significant advancements in robustness against input perturbations have been realized for deep neural networks (DNNs) through the application of adversarial training techniques. However, implementing these methods for perception tasks in unmanned veh...
Minimizing computational overhead in healthcare time-series classification remains a critical challenge, particularly for deep learning models operating on high-dimensional sequential data under resource and latency constraints. This challenge is fur...
Despite the advance in deep learning technology, assuring the robustness of deep neural networks (DNNs) is challenging and necessary in safety-critical environments, including automobiles, IoT devices in smart factories, and medical devices, to name...
Pedestrian interaction remains a central safety challenge for autonomous driving, particularly under non-compliant or adversarial pedestrian behavior. Existing research and evaluations predominantly test against rule-following pedestrians, leaving a...
The rapid adoption of large language models (LLMs) in corporate and governmental systems has raised critical security concerns, particularly prompt injection attacks exploiting LLMs’ inability to differentiate control instructions from untruste...
Due to the distinctive attributes of underwater environments, underwater images frequently encounter challenges such as low contrast, color distortion, and noise. Current underwater image enhancement techniques often suffer from limited generalizatio...
Data-driven decision-making is the process of using data to inform your decision-making process and validate a course of action before committing to it. The quality of unlabeled data in real-world scenarios presents challenges for semi-supervised lea...
Flexible metamaterial absorbers have exhibited tremendous potential for applications in intelligent wearable devices and radar stealth protection due to their remarkable electromagnetic response characteristics and mechanical conformal adaptability....
Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. This poses a serious problem when applied to high-stakes applications. To solve this issue, uncertainty quantification (UQ) models have be...
Although neural network-based methods have significantly advanced the field of machine fault diagnosis, they remain vulnerable to physical adversarial attacks. This work investigates such attacks in the physical context of a real production line. Att...
Highway ramp merging is a safety-critical interaction for automated-vehicle validation because gap acceptance, longitudinal control, and lane-changing responses are coupled within a short time horizon. This paper presents a rule-distillation framewor...
Single Image Super-Resolution (SISR) for image enhancement enables the generation of high spatial resolution in Remote Sensing (RS) images without incurring additional costs. This approach offers a practical solution to obtain high-resolution RS imag...
The paper introduces a novel framework for detecting adversarial attacks on machine learning models that classify tabular data. Its purpose is to provide a robust method for the monitoring and continuous auditing of machine learning models for the pu...
Graph representation learning models inherently capture structural and attribute correlations, which may lead to significant privacy risks under attribute inference attacks. Existing privacy-preserving methods mainly focus on post hoc suppression of...
Infrared and visible image fusion plays a critical role in multimodal perception systems, particularly under challenging conditions such as low illumination, occlusion, or complex backgrounds. However, existing approaches often struggle with global f...
The Internet of Vehicles (IoV) connects vehicles, roadside infrastructure, and cloud or edge platforms to support safer and more efficient transportation. This connectivity also exposes vehicular networks to message spoofing, denial-of-service, and m...
Adversarial transfer learning is extensively applied in computer vision owing to its remarkable capability in addressing domain adaptation. However, its applications in credit scoring remain underexplored due to the complexity of financial data. The...
Accurate crude oil price forecasting is essential, considering oil’s critical role in the global economy. However, the crude oil market is significantly influenced by external, transient events, posing challenges in capturing price fluctuations...
Smart city operation assumes dynamic infrastructure in various aspects. However, organization and process modelling require domain expertise and significant efforts from modelers. As a result, such processes are still not well supported by IT systems...
As the reservoir and its attribute distribution are obviously controlled by sedimentary facies, the facies modeling is one of the important bases for delineating the area of high-quality reservoir and characterizing the attribute parameter distributi...
The standard matrix factorization methods for recommender systems suffer from data sparsity and cold-start problems. Thus, in real-world scenarios where items are commonly associated with textual data such as reviews, it becomes necessary to build a...
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