pwc-archive/papers-with-abstracts huggingface dataset:
Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS
Authors: Taneya Sharma, Seyed Ahmad Soleymani, Mohammad Shojafar, Rahim Tafazolli
Date: 2025-01-31 00:00:00
Tasks: Intrusion Detection

This paper introduces a secure communication architecture for Unmanned Aerial Vehicles (UAVs) and ground stations in 5G networks, addressing critical challenges in network security. The proposed solution integrates the Advanced Encryption Standard (AES) with Elliptic Curve Cryptography (ECC) and CRYSTALS-Kyber for key encapsulation, offering a hybrid cryptographic approach. By incorporating CRYSTALS-Kyber, the framework mitigates vulnerabilities in ECC against quantum attacks, positioning it as a quantum-resistant alternative. The architecture is based on a server-client model, with UAVs functioning as clients and the ground station acting as the server. The system was rigorously evaluated in both VPN and 5G environments. Experimental results confirm that CRYSTALS-Kyber delivers strong protection against quantum threats with minimal performance overhead, making it highly suitable for UAVs with resource constraints. Moreover, the proposed architecture integrates an Artificial Intelligence (AI)-based Intrusion Detection System (IDS) to further enhance security. In performance evaluations, the IDS demonstrated strong results across multiple models with XGBoost, particularly in more demanding scenarios, outperforming other models with an accuracy of 97.33% and an AUC of 0.94. These findings underscore the potential of combining quantum-resistant encryption mechanisms with AI-driven IDS to create a robust, scalable, and secure communication framework for UAV networks, particularly within the high-performance requirements of 5G environments.
Enhancing Model Defense Against Jailbreaks with Proactive Safety Reasoning
Authors: Xianglin Yang, Gelei Deng, Jieming Shi, Tianwei Zhang, Jin Song Dong
Date: 2025-01-31 00:00:00
Tasks: Blocking, Safety Alignment

Large language models (LLMs) are vital for a wide range of applications yet remain susceptible to jailbreak threats, which could lead to the generation of inappropriate responses. Conventional defenses, such as refusal and adversarial training, often fail to cover corner cases or rare domains, leaving LLMs still vulnerable to more sophisticated attacks. We propose a novel defense strategy, Safety Chain-of-Thought (SCoT), which harnesses the enhanced \textit{reasoning capabilities} of LLMs for proactive assessment of harmful inputs, rather than simply blocking them. SCoT augments any refusal training datasets to critically analyze the intent behind each request before generating answers. By employing proactive reasoning, SCoT enhances the generalization of LLMs across varied harmful queries and scenarios not covered in the safety alignment corpus. Additionally, it generates detailed refusals specifying the rules violated. Comparative evaluations show that SCoT significantly surpasses existing defenses, reducing vulnerability to out-of-distribution issues and adversarial manipulations while maintaining strong general capabilities.
Logical Modalities within the European AI Act: An Analysis
Authors: Lara Lawniczak, Christoph Benzmüller
Date: 2025-01-31 00:00:00

The paper presents a comprehensive analysis of the European AI Act in terms of its logical modalities, with the aim of preparing its formal representation, for example, within the logic-pluralistic Knowledge Engineering Framework and Methodology (LogiKEy). LogiKEy develops computational tools for normative reasoning based on formal methods, employing Higher-Order Logic (HOL) as a unifying meta-logic to integrate diverse logics through shallow semantic embeddings. This integration is facilitated by Isabelle/HOL, a proof assistant tool equipped with several automated theorem provers. The modalities within the AI Act and the logics suitable for their representation are discussed. For a selection of these logics, embeddings in HOL are created, which are then used to encode sample paragraphs. Initial experiments evaluate the suitability of these embeddings for automated reasoning, and highlight key challenges on the way to more robust reasoning capabilities.
Swarm-Gen: Fast Generation of Diverse Feasible Swarm Behaviors
Authors: Simon Idoko, B. Bhanu Teja, K. Madhava Krishna, Arun Kumar Singh
Date: 2025-01-31 00:00:00

Coordination behavior in robot swarms is inherently multi-modal in nature. That is, there are numerous ways in which a swarm of robots can avoid inter-agent collisions and reach their respective goals. However, the problem of generating diverse and feasible swarm behaviors in a scalable manner remains largely unaddressed. In this paper, we fill this gap by combining generative models with a safety-filter (SF). Specifically, we sample diverse trajectories from a learned generative model which is subsequently projected onto the feasible set using the SF. We experiment with two choices for generative models, namely: Conditional Variational Autoencoder (CVAE) and Vector-Quantized Variational Autoencoder (VQ-VAE). We highlight the trade-offs these two models provide in terms of computation time and trajectory diversity. We develop a custom solver for our SF and equip it with a neural network that predicts context-specific initialization. Thecinitialization network is trained in a self-supervised manner, taking advantage of the differentiability of the SF solver. We provide two sets of empirical results. First, we demonstrate that we can generate a large set of multi-modal, feasible trajectories, simulating diverse swarm behaviors, within a few tens of milliseconds. Second, we show that our initialization network provides faster convergence of our SF solver vis-a-vis other alternative heuristics.
Divergent Emotional Patterns in Disinformation on Social Media? An Analysis of Tweets and TikToks about the DANA in Valencia
Authors: Iván Arcos, Paolo Rosso, Ramón Salaverría
Date: 2025-01-28 00:00:00
Tasks: Emotion Recognition

This study investigates the dissemination of disinformation on social media platforms during the DANA event (DANA is a Spanish acronym for Depresion Aislada en Niveles Altos, translating to high-altitude isolated depression) that resulted in extremely heavy rainfall and devastating floods in Valencia, Spain, on October 29, 2024. We created a novel dataset of 650 TikTok and X posts, which was manually annotated to differentiate between disinformation and trustworthy content. Additionally, a Few-Shot annotation approach with GPT-4o achieved substantial agreement (Cohen's kappa of 0.684) with manual labels. Emotion analysis revealed that disinformation on X is mainly associated with increased sadness and fear, while on TikTok, it correlates with higher levels of anger and disgust. Linguistic analysis using the LIWC dictionary showed that trustworthy content utilizes more articulate and factual language, whereas disinformation employs negations, perceptual words, and personal anecdotes to appear credible. Audio analysis of TikTok posts highlighted distinct patterns: trustworthy audios featured brighter tones and robotic or monotone narration, promoting clarity and credibility, while disinformation audios leveraged tonal variation, emotional depth, and manipulative musical elements to amplify engagement. In detection models, SVM+TF-IDF achieved the highest F1-Score, excelling with limited data. Incorporating audio features into roberta-large-bne improved both Accuracy and F1-Score, surpassing its text-only counterpart and SVM in Accuracy. GPT-4o Few-Shot also performed well, showcasing the potential of large language models for automated disinformation detection. These findings demonstrate the importance of leveraging both textual and audio features for improved disinformation detection on multimodal platforms like TikTok.
Region of Interest-Focused Dynamic Enhancement (RoIDE) for Satellite Images
Authors: Trong-An Bui, Pei-Jun Lee, John Liobe, Vaidotas Barzdenas, Dainius Udris
Date: 2025-01-03 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2025 1

A major challenge in satellite imaging is the wide-ranging brightness levels within a single frame, which can lead to inadequate exposure in shaded regions and overexposure in bright areas. This research proposes a two-stage deep learning architecture, region of interest-focused dynamic enhancement (RoIDE), for multiexposure generation (MEG) and fusion. The first stage generates multiple exposures from a single standard dynamic range (SDR) image, capturing details across different brightness levels. The second stage fuses these images to create a high-contrast, comprehensive image, preserving details in both low and high-exposure regions. This approach allows for focused processing, enhancing regions of interest (RoI) without causing overexposure. Experimental results show significant improvements in the dynamic range of satellite images, particularly in RoIs. Enhanced visibility and detail preservation in both dark and bright areas are achieved, demonstrating the effectiveness of the RoIDE architecture. Nonreference metrics, including perception-based image quality evaluator (PIQE) (34.92), and blind tone-mapped quality index (BTMQI) (0.54), confirm the model’s superior perceptual, spatial, naturalness, and tone-mapped quality. Additionally, image quality assessments using peak signal-to-noise ratio (PSNR) (30.43), learned perceptual image patch similarity (LPIPS) (0.168), and VDP-3 (9.35) show significant improvements over other state-of-the-art models. These advancements are crucial for accurate data analysis in applications relying on satellite imagery.
Improving YOLOv8 with Scattering Transform and Attention for Maritime Awareness
Authors: Borja Carrillo-Perez, Angel Bueno Rodriguez, Sarah Barnes, Maurice Stephan
Date: 2023-10-19 00:00:00
Proceeding: International Symposium on Image and Signal Processing and Analysis (ISPA) 2023 10

Ship recognition and georeferencing using monitoring cameras are crucial to many applications in maritime situational awareness. Although deep learning algorithms are available for ship recognition tasks, there is a need for innovative approaches that attain higher precision rates irrespective of ship sizes, types, or physical hardware limitations. Furthermore, their deployment in maritime environments requires embedded systems capable of image processing, with balanced accuracy, reduced latency and low energy consumption. To achieve that, we build upon the foundations of the standard YOLOv8 and present a novel architecture that improves the segmentation and georeferencing of ships in the context of maritime awareness using a real-world dataset (ShipSG). Our architecture synergizes global and local features in the image for improved ship segmentation and georeferencing. The 2D scattering-transform enhances the YOLOv8 backbone by extracting global structural features from the image. The addition of convolutional block attention module (CBAM) in the head allows focusing on relevant spatial and channel-wise regions. We achieve mAP of 75.46%, comparable to larger YOLOv8 models at a much faster inference speed, 59.3 milliseconds per image, when deployed on the NVIDIA Jetson Xavier AGX as target embedded system. We applied the modified network to georeference the segmented ship masks, with a georeferencing distance error of 18 meters, which implies comparable georeferencing performance to non-embedded approaches.
Enhanced Small Ship Segmentation with Optimized ScatYOLOv8+CBAM on Embedded Systems
Authors: Borja Carrillo-Perez, Angel Bueno Rodriguez, Sarah Barnes, Maurice Stephan
Date: 2024-09-13 00:00:00
Proceeding: IEEE International Conference on Real-time Computing and Robotics (RCAR) 2024 9
Tasks: Segmentation

To enhance maritime situational awareness, real-time segmentation of small or distant ships from optical monitoring footage, poses significant performance challenges, especially on embedded systems. Efficient processing of full-resolution images is essential for precise small ship segmentation. In this paper, we introduce a framework that combines an optimized version of ScatYOLOv8+CBAM with a custom batch-processed Slicing Aided Hyper Inference (SAHI). This approach is aimed at efficient and accurate small ship segmentation, deployed on embedded systems, and is validated using a real-world maritime dataset (ShipSG). With our optimized ScatYOLOv8+CBAM, we substantially improve inference efficiency with a 36% faster inference speed compared to its predecessor in the lightest model size, without compromising segmentation accuracy. Additionally, the integration of batch-processed SAHI, despite an increase in computation time, improves the accuracy of small ship segmentation up to 11%, allowing more effective utilization of full-resolution imagery without compromising the computational resources of embedded platforms. Our findings set a new benchmark for embedded maritime monitoring and pave the way for future research to optimize real-time high-resolution processing in resource-constrained environments.
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets
Authors: Kashish Sharma, Borja Carrillo-Perez, Sarah Barnes
Date: 2024-11-25 00:00:00
Proceeding: 4th European Workshop on Maritime Systems, Resilience and Security (MARESEC) 2024 11
Tasks: Diversity, Object Recognition, Unity

Computer vision for real-world applications faces data acquisition challenges, including accessibility, high costs, difficulty in obtaining diversity in scenarios or environmental conditions. Synthetic data usage has surged as a solution to these obstacles. Leveraging game engines for synthetic dataset creation effectively enriches training datasets with increased diversity and richness. The choice of the game engine, pivotal for generating photorealistic simulations, may influence synthetic data quality. This study compares Unity Engine’s and Unreal Engine’s capabilities in generating synthetic maritime datasets to support ship recognition applications. To this end, the realworld maritime dataset ShipSG has been replicated in the corresponding game engines to create the same scenarios. The performance of the generated synthetic datasets is benchmarked against the real-world ShipSG dataset using the object recognition model YOLOv8. Furthermore, the comparison evaluates various photorealistic parameters found in the dataset images to determine the optimal configuration for improving performance with YOLOv8. The datasets generated using the Unity Engine, with all photorealistic effects present and the one with no lens distortion, achieved the highest accuracy in ship recognition with a mAP of 72.3%. Both configurations of the synthetic datasets were utilised to augment the ShipSG dataset to train YOLOv8. The configuration with all photorealistic parameters in place provides the highest mAP increase, of 0.4% compared with YOLOv8 performance on ShipSG when no synthetic data is used. This evidence underscores that utilising game engines can effectively support and enhance ship recognition tasks.
Integrity Assessment of Maritime Object Detection Impacted by Partial Camera Obstruction
Authors: Felipe A. Costa de Oliveira, Borja Carrillo-Perez, Alberto García-Ortiz, Frank Sill Torres
Date: 2024-01-08 00:00:00
Proceeding: 7th International Conference on System Reliability and Safety (ICSRS) 2024 1
Tasks: Decision Making, Object, object-detection, Object Detection

The performance and usage of machine learning based object detection in visual data has increased significantly in the past decade. This technology can enable automated decision making in various applications, extracting key information in real time from a camera-based monitoring solution. However, to ensure the resilience and dependability in a safety-critical application, the intelligence provided by the monitoring solution must be reliable. In this context, understanding the impact of potential disturbances in the object detection performance is important. This work conducts an integrity assessment of maritime object detection under partial camera obstruction events. A subset of the ShipSG dataset, which contains thousands of annotated and segmented ship images from a harbor, and the Faster-RCNN object detection algorithm, trained on the seven different ship classes of the dataset, were used. The effect of simulated obstructions, of various intensities and configurations, on the false-positive, misclassification, false negative ratios, and associated detection score distributions were investigated. The outcome suggests that the use of a partial obstruction detection step, and the consideration of that information, can mitigate the consequences of faults in the object detection.
Dynamics-incorporated Modeling Framework for Stability Constrained Scheduling Under High-penetration of Renewable Energy
Authors: Jinning Wang, Fangxing Li, Xin Fang, Hantao Cui, Buxin She, Hang Shuai, Qiwei Zhang, Kevin Tomsovic
Date: 2025-01-10 00:00:00
Proceeding: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2025 1
Tasks: Scheduling

In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy.
Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare
Authors: Hang Zhang, Qian Lou, Yanshan Wang
Date: 2025-01-27 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Safety Alignment

Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques within medical contexts. To quantify the effectiveness of these techniques, we propose an automated and domain-adapted agentic evaluation pipeline. Experiment results indicate that leading commercial and open-source LLMs are highly vulnerable to medical jailbreaking attacks. To bolster model safety and reliability, we further investigate the effectiveness of Continual Fine-Tuning (CFT) in defending against medical adversarial attacks. Our findings underscore the necessity for evolving attack methods evaluation, domain-specific safety alignment, and LLM safety-utility balancing. This research offers actionable insights for advancing the safety and reliability of AI clinicians, contributing to ethical and effective AI deployment in healthcare.
The Relationship Between Network Similarity and Transferability of Adversarial Attacks
Authors: Gerrit Klause, Niklas Bunzel
Date: 2025-01-27 00:00:00
Tasks: Adversarial Attack

Neural networks are vulnerable to adversarial attacks, and several defenses have been proposed. Designing a robust network is a challenging task given the wide range of attacks that have been developed. Therefore, we aim to provide insight into the influence of network similarity on the success rate of transferred adversarial attacks. Network designers can then compare their new network with existing ones to estimate its vulnerability. To achieve this, we investigate the complex relationship between network similarity and the success rate of transferred adversarial attacks. We applied the Centered Kernel Alignment (CKA) network similarity score and used various methods to find a correlation between a large number of Convolutional Neural Networks (CNNs) and adversarial attacks. Network similarity was found to be moderate across different CNN architectures, with more complex models such as DenseNet showing lower similarity scores due to their architectural complexity. Layer similarity was highest for consistent, basic layers such as DataParallel, Dropout and Conv2d, while specialized layers showed greater variability. Adversarial attack success rates were generally consistent for non-transferred attacks, but varied significantly for some transferred attacks, with complex networks being more vulnerable. We found that a DecisionTreeRegressor can predict the success rate of transferred attacks for all black-box and Carlini & Wagner attacks with an accuracy of over 90%, suggesting that predictive models may be viable under certain conditions. However, the variability of results across different data subsets underscores the complexity of these relationships and suggests that further research is needed to generalize these findings across different attack scenarios and network architectures.
Indiana Jones: There Are Always Some Useful Ancient Relics
Authors: Junchen Ding, Jiahao Zhang, Yi Liu, Ziqi Ding, Gelei Deng, Yuekang Li
Date: 2025-01-27 00:00:00

This paper introduces Indiana Jones, an innovative approach to jailbreaking Large Language Models (LLMs) by leveraging inter-model dialogues and keyword-driven prompts. Through orchestrating interactions among three specialised LLMs, the method achieves near-perfect success rates in bypassing content safeguards in both white-box and black-box LLMs. The research exposes systemic vulnerabilities within contemporary models, particularly their susceptibility to producing harmful or unethical outputs when guided by ostensibly innocuous prompts framed in historical or contextual contexts. Experimental evaluations highlight the efficacy and adaptability of Indiana Jones, demonstrating its superiority over existing jailbreak methods. These findings emphasise the urgent need for enhanced ethical safeguards and robust security measures in the development of LLMs. Moreover, this work provides a critical foundation for future studies aimed at fortifying LLMs against adversarial exploitation while preserving their utility and flexibility.
The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs
Authors: Sergey Berezin, Reza Farahbakhsh, Noel Crespi
Date: 2025-01-27 00:00:00

We present a novel class of jailbreak adversarial attacks on LLMs, termed Task-in-Prompt (TIP) attacks. Our approach embeds sequence-to-sequence tasks (e.g., cipher decoding, riddles, code execution) into the model's prompt to indirectly generate prohibited inputs. To systematically assess the effectiveness of these attacks, we introduce the PHRYGE benchmark. We demonstrate that our techniques successfully circumvent safeguards in six state-of-the-art language models, including GPT-4o and LLaMA 3.2. Our findings highlight critical weaknesses in current LLM safety alignments and underscore the urgent need for more sophisticated defence strategies. Warning: this paper contains examples of unethical inquiries used solely for research purposes.