pwc-archive/papers-with-abstracts huggingface dataset:
Team NYCU at Defactify4: Robust Detection and Source Identification of AI-Generated Images Using CNN and CLIP-Based Models
Authors: Tsan-Tsung Yang, I-Wei Chen, Kuan-Ting Chen, Shang-Hsuan Chiang, Wen-Chih Peng
Date: 2025-03-13 00:00:00
Tasks: Misinformation

With the rapid advancement of generative AI, AI-generated images have become increasingly realistic, raising concerns about creativity, misinformation, and content authenticity. Detecting such images and identifying their source models has become a critical challenge in ensuring the integrity of digital media. This paper tackles the detection of AI-generated images and identifying their source models using CNN and CLIP-ViT classifiers. For the CNN-based classifier, we leverage EfficientNet-B0 as the backbone and feed with RGB channels, frequency features, and reconstruction errors, while for CLIP-ViT, we adopt a pretrained CLIP image encoder to extract image features and SVM to perform classification. Evaluated on the Defactify 4 dataset, our methods demonstrate strong performance in both tasks, with CLIP-ViT showing superior robustness to image perturbations. Compared to baselines like AEROBLADE and OCC-CLIP, our approach achieves competitive results. Notably, our method ranked Top-3 overall in the Defactify 4 competition, highlighting its effectiveness and generalizability. All of our implementations can be found in https://github.com/uuugaga/Defactify_4
Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions
Authors: Quanyuan Ruan, Jiabao Lei, Wenhao Yuan, Yanglin Zhang, Dekun Lu, Guiliang Liu, Kui Jia
Date: 2025-03-14 00:00:00
Proceeding: CVPR 2025 1

Differentiable rendering has gained significant attention in the field of robotics, with differentiable robot rendering emerging as an effective paradigm for learning robotic actions from image-space supervision. However, the lack of physical world perception in this approach may lead to potential collisions during action optimization. In this work, we introduce a novel improvement on previous efforts by incorporating physical awareness of collisions through the learning of a neural robotic collision classifier. This enables the optimization of actions that avoid collisions with static, non-interactable environments as well as the robot itself. To facilitate effective gradient optimization with the classifier, we identify the underlying issue and propose leveraging Eikonal regularization to ensure consistent gradients for optimization. Our solution can be seamlessly integrated into existing differentiable robot rendering frameworks, utilizing gradients for optimization and providing a foundation for future applications of differentiable rendering in robotics with improved reliability of interactions with the physical world. Both qualitative and quantitative experiments demonstrate the necessity and effectiveness of our method compared to previous solutions.
DSTIGCN: Deformable Spatial-Temporal Interaction Graph Convolution Network for Pedestrian Trajectory Prediction
Authors: Wangxing Chen, Haifeng Sang, Jinyu Wang, Zishan Zhao
Date: 2025-01-16 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2025 1
Tasks: Autonomous Driving, Pedestrian Trajectory Prediction, Prediction, TAG, Trajectory Prediction

Accurate and reliable pedestrian trajectory prediction can reduce the risk of human-vehicle collisions and predict accidents in advance, which is crucial for developing autonomous driving and intelligent monitoring. Previous trajectory prediction methods face two common problems: 1. ignoring the joint modeling of pedestrians’ complex spatial-temporal interactions, and 2. suffering from the long-tail effect, which prevents accurate capture of the diversity of pedestrians’ future movements. To address these problems, we propose a Deformable Spatial-Temporal Interaction Graph Convolution Network (DSTIGCN). First, we construct a spatial graph and employ the attention mechanism to preliminarily describe the spatial interactions of pedestrians at each moment. To solve problem 1, we design a deformable spatial-temporal interaction module. The module autonomously learns the spatial-temporal interaction relationships of pedestrians through the offset of multiple asymmetric deformable convolution kernels in both spatial and temporal dimensions, thereby achieving joint modeling of complex spatial-temporal interactions. Next, we obtain trajectory representation features through graph convolution and then predict the two-dimensional Gaussian distribution parameters of future trajectories using the Temporal Attention-Gated Temporal Convolution Network (TAG-TCN). To address problem 2, we introduce Latin hypercube sampling to sample the two-dimensional Gaussian distribution of future trajectories, thereby improving the multi-modal prediction effect of the model under limited samples. Experiments on ETH, UCY, and SDD datasets have verified that our method can achieve high-precision prediction of pedestrian future trajectories under limited parameters.
Multimodal-Aware Fusion Network for Referring Remote Sensing Image Segmentation
Authors: Leideng Shi, Juan Zhang
Date: 2025-03-14 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Referring remote sensing image segmentation (RRSIS) is a novel visual task in remote sensing images segmentation, which aims to segment objects based on a given text description, with great significance in practical application. Previous studies fuse visual and linguistic modalities by explicit feature interaction, which fail to effectively excavate useful multimodal information from dual-branch encoder. In this letter, we design a multimodal-aware fusion network (MAFN) to achieve fine-grained alignment and fusion between the two modalities. We propose a correlation fusion module (CFM) to enhance multi-scale visual features by introducing adaptively noise in transformer, and integrate cross-modal aware features. In addition, MAFN employs multi-scale refinement convolution (MSRC) to adapt to the various orientations of objects at different scales to boost their representation ability to enhances segmentation accuracy. Extensive experiments have shown that MAFN is significantly more effective than the state of the art on RRSIS-D datasets. The source code is available at https://github.com/Roaxy/MAFN.
A Survey on Knowledge-Oriented Retrieval-Augmented Generation
Authors: Mingyue Cheng, Yucong Luo, Jie Ouyang, Qi Liu, Huijie Liu, Li Li, Shuo Yu, Bohou Zhang, Jiawei Cao, Jie Ma, Daoyu Wang
Date: 2025-03-11 00:00:00
Tasks: Information Retrieval, Natural Language Understanding, Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Survey

Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-scale retrieval systems with generative models. RAG leverages external knowledge sources, such as documents, databases, or structured data, to improve model performance and generate more accurate and contextually relevant outputs. This survey aims to provide a comprehensive overview of RAG by examining its fundamental components, including retrieval mechanisms, generation processes, and the integration between the two. We discuss the key characteristics of RAG, such as its ability to augment generative models with dynamic external knowledge, and the challenges associated with aligning retrieved information with generative objectives. We also present a taxonomy that categorizes RAG methods, ranging from basic retrieval-augmented approaches to more advanced models incorporating multi-modal data and reasoning capabilities. Additionally, we review the evaluation benchmarks and datasets commonly used to assess RAG systems, along with a detailed exploration of its applications in fields such as question answering, summarization, and information retrieval. Finally, we highlight emerging research directions and opportunities for improving RAG systems, such as enhanced retrieval efficiency, model interpretability, and domain-specific adaptations. This paper concludes by outlining the prospects for RAG in addressing real-world challenges and its potential to drive further advancements in natural language processing.
Video Anomaly Detection with Structured Keywords
Authors: Thomas Foltz
Date: 2025-03-07 00:00:00
Tasks: Anomaly Classification, Anomaly Detection, Video Anomaly Detection

This paper focuses on detecting anomalies in surveillance video using keywords by leveraging foundational models' feature representation generalization capabilities. We present a novel, lightweight pipeline for anomaly classification using keyword weights. Our pipeline employs a two-stage process: induction followed by deduction. In induction, descriptions are generated from normal and anomalous frames to identify and assign weights to relevant keywords. In deduction, inference frame descriptions are converted into keyword encodings using induction-derived weights for input into our neural network for anomaly classification. We achieved comparable performance on the three benchmarks UCSD Ped2, Shanghai Tech, and CUHK Avenue, with ROC AUC scores of 0.865, 0.745, and 0.742, respectively. These results are achieved without temporal context, making such a system viable for real-time applications. Our model improves implementation setup, interpretability, and inference speed for surveillance devices on the edge, introducing a performance trade-off against other video anomaly detection systems. As the generalization capabilities of open-source foundational models improve, our model demonstrates that the exclusive use of text for feature representations is a promising direction for efficient real-time interpretable video anomaly detection.
Bottom-up Iterative Anomalous Diffusion Detector (BI-ADD)
Authors: Junwoo Park, Nataliya Sokolovska, Clément Cabriel, Ignacio Izeddin, Judith Miné-Hattab
Date: 2025-03-14 00:00:00

In recent years, the segmentation of short molecular trajectories with varying diffusive properties has drawn particular attention of researchers, since it allows studying the dynamics of a particle. In the past decade, machine learning methods have shown highly promising results, also in changepoint detection and segmentation tasks. Here, we introduce a novel iterative method to identify the changepoints in a molecular trajectory, i.e., frames, where the diffusive behavior of a particle changes. A trajectory in our case follows a fractional Brownian motion and we estimate the diffusive properties of the trajectories. The proposed BI-ADD combines unsupervised and supervised learning methods to detect the changepoints. Our approach can be used for the analysis of molecular trajectories at the individual level and also be extended to multiple particle tracking, which is an important challenge in fundamental biology. We validated BI-ADD in various scenarios within the framework of the AnDi2 Challenge 2024 dedicated to single particle tracking. Our method is implemented in Python and is publicly available for research purposes.
Unmasking the Shadows of AI: Investigating Deceptive Capabilities in Large Language Models
Authors: Linge Guo
Date: 2024-02-07 00:00:00

This research critically navigates the intricate landscape of AI deception, concentrating on deceptive behaviours of Large Language Models (LLMs). My objective is to elucidate this issue, examine the discourse surrounding it, and subsequently delve into its categorization and ramifications. The essay initiates with an evaluation of the AI Safety Summit 2023 (ASS) and introduction of LLMs, emphasising multidimensional biases that underlie their deceptive behaviours.The literature review covers four types of deception categorised: Strategic deception, Imitation, Sycophancy, and Unfaithful Reasoning, along with the social implications and risks they entail. Lastly, I take an evaluative stance on various aspects related to navigating the persistent challenges of the deceptive AI. This encompasses considerations of international collaborative governance, the reconfigured engagement of individuals with AI, proposal of practical adjustments, and specific elements of digital education.
Cost-effective Deep Learning Infrastructure with NVIDIA GPU
Authors: Aatiz Ghimire, Shahnawaz Alam, Siman Giri, Madhav Prasad Ghimire
Date: 2025-03-14 00:00:00
Tasks: Deep Learning, GPU, Scheduling

The growing demand for computational power is driven by advancements in deep learning, the increasing need for big data processing, and the requirements of scientific simulations for academic and research purposes. Developing countries like Nepal often struggle with the resources needed to invest in new and better hardware for these purposes. However, optimizing and building on existing technology can still meet these computing demands effectively. To address these needs, we built a cluster using four NVIDIA GeForce GTX 1650 GPUs. The cluster consists of four nodes: one master node that controls and manages the entire cluster, and three compute nodes dedicated to processing tasks. The master node is equipped with all necessary software for package management, resource scheduling, and deployment, such as Anaconda and Slurm. In addition, a Network File Storage (NFS) system was integrated to provide the additional storage required by the cluster. Given that the cluster is accessible via ssh by a public domain address, which poses significant cybersecurity risks, we implemented fail2ban to mitigate brute force attacks and enhance security. Despite the continuous challenges encountered during the design and implementation process, this project demonstrates how powerful computational clusters can be built to handle resource-intensive tasks in various demanding fields.
Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding
Authors: Yifang Chen, Xiaoyu Li, YIngyu Liang, Zhenmei Shi, Zhao Song, Yu Tian
Date: 2025-03-14 00:00:00

The key-value (KV) cache in the tensor version of transformers presents a significant bottleneck during inference. While previous work analyzes the fundamental space complexity barriers in standard attention mechanisms [Haris and Onak, 2025], our work generalizes the space complexity barriers result to tensor attention version. Our theoretical contributions rely on a reduction from communication complexity and deduce the memory lower bound for tensor-structured attention mechanisms when $d = \Omega(\log n)$. Furthermore, we introduce two types of tensor attention cache and present a trade-off between time and memory for two scenarios. Overall, our work provides a theoretical foundation for us to understand the time-memory tradeoff of KV-Cache compression in tensor attention decoding and offers more perspectives in developing more memory-efficient tensor attention Transformer architectures.
Predicting Stock Movement with BERTweet and Transformers
Authors: Michael Charles Albada, Mojolaoluwa Joshua Sonola
Date: 2025-03-13 00:00:00
Tasks: Deep Learning, Graph Attention

Applying deep learning and computational intelligence to finance has been a popular area of applied research, both within academia and industry, and continues to attract active attention. The inherently high volatility and non-stationary of the data pose substantial challenges to machine learning models, especially so for today's expressive and highly-parameterized deep learning models. Recent work has combined natural language processing on data from social media to augment models based purely on historic price data to improve performance has received particular attention. Previous work has achieved state-of-the-art performance on this task by combining techniques such as bidirectional GRUs, variational autoencoders, word and document embeddings, self-attention, graph attention, and adversarial training. In this paper, we demonstrated the efficacy of BERTweet, a variant of BERT pre-trained specifically on a Twitter corpus, and the transformer architecture by achieving competitive performance with the existing literature and setting a new baseline for Matthews Correlation Coefficient on the Stocknet dataset without auxiliary data sources.
Synthesizing Access Control Policies using Large Language Models
Authors: Adarsh Vatsa, Pratyush Patel, William Eiers
Date: 2025-03-14 00:00:00
Tasks: Management

Cloud compute systems allow administrators to write access control policies that govern access to private data. While policies are written in convenient languages, such as AWS Identity and Access Management Policy Language, manually written policies often become complex and error prone. In this paper, we investigate whether and how well Large Language Models (LLMs) can be used to synthesize access control policies. Our investigation focuses on the task of taking an access control request specification and zero-shot prompting LLMs to synthesize a well-formed access control policy which correctly adheres to the request specification. We consider two scenarios, one which the request specification is given as a concrete list of requests to be allowed or denied, and another in which a natural language description is used to specify sets of requests to be allowed or denied. We then argue that for zero-shot prompting, more precise and structured prompts using a syntax based approach are necessary and experimentally show preliminary results validating our approach.
Towards A Correct Usage of Cryptography in Semantic Watermarks for Diffusion Models
Authors: Jonas Thietke, Andreas Müller, Denis Lukovnikov, Asja Fischer, Erwin Quiring
Date: 2025-03-14 00:00:00
Tasks: Management

Semantic watermarking methods enable the direct integration of watermarks into the generation process of latent diffusion models by only modifying the initial latent noise. One line of approaches building on Gaussian Shading relies on cryptographic primitives to steer the sampling process of the latent noise. However, we identify several issues in the usage of cryptographic techniques in Gaussian Shading, particularly in its proof of lossless performance and key management, causing ambiguity in follow-up works, too. In this work, we therefore revisit the cryptographic primitives for semantic watermarking. We introduce a novel, general proof of lossless performance based on IND\$-CPA security for semantic watermarks. We then discuss the configuration of the cryptographic primitives in semantic watermarks with respect to security, efficiency, and generation quality.
Align in Depth: Defending Jailbreak Attacks via Progressive Answer Detoxification
Authors: Yingjie Zhang, Tong Liu, Zhe Zhao, Guozhu Meng, Kai Chen
Date: 2025-03-14 00:00:00
Tasks: Safety Alignment

Large Language Models (LLMs) are vulnerable to jailbreak attacks, which use crafted prompts to elicit toxic responses. These attacks exploit LLMs' difficulty in dynamically detecting harmful intents during the generation process. Traditional safety alignment methods, often relying on the initial few generation steps, are ineffective due to limited computational budget. This paper proposes DEEPALIGN, a robust defense framework that fine-tunes LLMs to progressively detoxify generated content, significantly improving both the computational budget and effectiveness of mitigating harmful generation. Our approach uses a hybrid loss function operating on hidden states to directly improve LLMs' inherent awareness of toxity during generation. Furthermore, we redefine safe responses by generating semantically relevant answers to harmful queries, thereby increasing robustness against representation-mutation attacks. Evaluations across multiple LLMs demonstrate state-of-the-art defense performance against six different attack types, reducing Attack Success Rates by up to two orders of magnitude compared to previous state-of-the-art defense while preserving utility. This work advances LLM safety by addressing limitations of conventional alignment through dynamic, context-aware mitigation.
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees
Authors: Juan Felipe Gomez, Bogdan Kulynych, Georgios Kaissis, Jamie Hayes, Borja Balle, Antti Honkela
Date: 2025-03-13 00:00:00
Tasks: image-classification, Image Classification

Current practices for reporting the level of differential privacy (DP) guarantees for machine learning (ML) algorithms provide an incomplete and potentially misleading picture of the guarantees and make it difficult to compare privacy levels across different settings. We argue for using Gaussian differential privacy (GDP) as the primary means of communicating DP guarantees in ML, with the full privacy profile as a secondary option in case GDP is too inaccurate. Unlike other widely used alternatives, GDP has only one parameter, which ensures easy comparability of guarantees, and it can accurately capture the full privacy profile of many important ML applications. To support our claims, we investigate the privacy profiles of state-of-the-art DP large-scale image classification, and the TopDown algorithm for the U.S. Decennial Census, observing that GDP fits the profiles remarkably well in all three cases. Although GDP is ideal for reporting the final guarantees, other formalisms (e.g., privacy loss random variables) are needed for accurate privacy accounting. We show that such intermediate representations can be efficiently converted to GDP with minimal loss in tightness.
Phishsense-1B: A Technical Perspective on an AI-Powered Phishing Detection Model
Authors: SE Blake
Date: 2025-03-13 00:00:00
Tasks: Prompt Engineering

Phishing is a persistent cybersecurity threat in today's digital landscape. This paper introduces Phishsense-1B, a refined version of the Llama-Guard-3-1B model, specifically tailored for phishing detection and reasoning. This adaptation utilizes Low-Rank Adaptation (LoRA) and the GuardReasoner finetuning methodology. We outline our LoRA-based fine-tuning process, describe the balanced dataset comprising phishing and benign emails, and highlight significant performance improvements over the original model. Our findings indicate that Phishsense-1B achieves an impressive 97.5% accuracy on a custom dataset and maintains strong performance with 70% accuracy on a challenging real-world dataset. This performance notably surpasses both unadapted models and BERT-based detectors. Additionally, we examine current state-of-the-art detection methods, compare prompt-engineering with fine-tuning strategies, and explore potential deployment scenarios.
Attacking Multimodal OS Agents with Malicious Image Patches
Authors: Lukas Aichberger, Alasdair Paren, Yarin Gal, Philip Torr, Adel Bibi
Date: 2025-03-13 00:00:00

Recent advances in operating system (OS) agents enable vision-language models to interact directly with the graphical user interface of an OS. These multimodal OS agents autonomously perform computer-based tasks in response to a single prompt via application programming interfaces (APIs). Such APIs typically support low-level operations, including mouse clicks, keyboard inputs, and screenshot captures. We introduce a novel attack vector: malicious image patches (MIPs) that have been adversarially perturbed so that, when captured in a screenshot, they cause an OS agent to perform harmful actions by exploiting specific APIs. For instance, MIPs embedded in desktop backgrounds or shared on social media can redirect an agent to a malicious website, enabling further exploitation. These MIPs generalise across different user requests and screen layouts, and remain effective for multiple OS agents. The existence of such attacks highlights critical security vulnerabilities in OS agents, which should be carefully addressed before their widespread adoption.