pwc-archive/papers-with-abstracts huggingface dataset:
X-SG$^2$S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
Authors: Zihang Cheng, Huiping Zhuang, Chun Li, Xin Meng, Ming Li, Fei Richard Yu, Liqiang Nie
Date: 2025-02-13 00:00:00
Tasks: 3D Generation, 3DGS, 3D Reconstruction

3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. Training to get a 3DGS scene often takes a lot of time and resources and even valuable inspiration. The increasing amount of 3DGS digital asset have brought great challenges to the copyright protection. However, it still lacks profound exploration targeted at 3DGS. In this paper, we propose a new framework X-SG$^2$S which can simultaneously watermark 1 to 3D messages while keeping the original 3DGS scene almost unchanged. Generally, we have a X-SG$^2$S injector for adding multi-modal messages simultaneously and an extractor for extract them. Specifically, we first split the watermarks into message patches in a fixed manner and sort the 3DGS points. A self-adaption gate is used to pick out suitable location for watermarking. Then use a XD(multi-dimension)-injection heads to add multi-modal messages into sorted 3DGS points. A learnable gate can recognize the location with extra messages and XD-extraction heads can restore hidden messages from the location recommended by the learnable gate. Extensive experiments demonstrated that the proposed X-SG$^2$S can effectively conceal multi modal messages without changing pretrained 3DGS pipeline or the original form of 3DGS parameters. Meanwhile, with simple and efficient model structure and high practicality, X-SG$^2$S still shows good performance in hiding and extracting multi-modal inner structured or unstructured messages. X-SG$^2$S is the first to unify 1 to 3D watermarking model for 3DGS and the first framework to add multi-modal watermarks simultaneous in one 3DGS which pave the wave for later researches.
Image Watermarking of Generative Diffusion Models
Authors: Yunzhuo Chen, Jordan Vice, Naveed Akhtar, Nur Al Hasan Haldar, Ajmal Mian
Date: 2025-02-12 00:00:00

Embedding watermarks into the output of generative models is essential for establishing copyright and verifiable ownership over the generated content. Emerging diffusion model watermarking methods either embed watermarks in the frequency domain or offer limited versatility of the watermark patterns in the image space, which allows simplistic detection and removal of the watermarks from the generated content. To address this issue, we propose a watermarking technique that embeds watermark features into the diffusion model itself. Our technique enables training of a paired watermark extractor for a generative model that is learned through an end-to-end process. The extractor forces the generator, during training, to effectively embed versatile, imperceptible watermarks in the generated content while simultaneously ensuring their precise recovery. We demonstrate highly accurate watermark embedding/detection and show that it is also possible to distinguish between different watermarks embedded with our method to differentiate between generative models.
Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach
Authors: Régnier Avice, Bernhard Haslhofer, Zhidong Li, Jianlong Zhou
Date: 2025-02-12 00:00:00
Tasks: Blocking, TAG

Attribution tags form the foundation of modern cryptoasset forensics. However, inconsistent or incorrect tags can mislead investigations and even result in false accusations. To address this issue, we propose a novel computational method based on Large Language Models (LLMs) to link attribution tags with well-defined knowledge graph concepts. We implemented this method in an end-to-end pipeline and conducted experiments showing that our approach outperforms baseline methods by up to 37.4% in F1-score across three publicly available attribution tag datasets. By integrating concept filtering and blocking procedures, we generate candidate sets containing five knowledge graph entities, achieving a recall of 93% without the need for labeled data. Additionally, we demonstrate that local LLM models can achieve F1-scores of 90%, comparable to remote models which achieve 94%. We also analyze the cost-performance trade-offs of various LLMs and prompt templates, showing that selecting the most cost-effective configuration can reduce costs by 90%, with only a 1% decrease in performance. Our method not only enhances attribution tag quality but also serves as a blueprint for fostering more reliable forensic evidence.
Trustworthy AI: Safety, Bias, and Privacy -- A Survey
Authors: Xingli Fang, Jianwei Li, Varun Mulchandani, Jung-eun Kim
Date: 2025-02-11 00:00:00
Tasks: Safety Alignment, Survey

The capabilities of artificial intelligence systems have been advancing to a great extent, but these systems still struggle with failure modes, vulnerabilities, and biases. In this paper, we study the current state of the field, and present promising insights and perspectives regarding concerns that challenge the trustworthiness of AI models. In particular, this paper investigates the issues regarding three thrusts: safety, privacy, and bias, which hurt models' trustworthiness. For safety, we discuss safety alignment in the context of large language models, preventing them from generating toxic or harmful content. For bias, we focus on spurious biases that can mislead a network. Lastly, for privacy, we cover membership inference attacks in deep neural networks. The discussions addressed in this paper reflect our own experiments and observations.
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning
Authors: Junfeng Guo, Yiming Li, Ruibo Chen, Yihan Wu, Chenxi Liu, Yanshuo Chen, Heng Huang
Date: 2025-02-10 00:00:00
Tasks: Anomaly Detection, RAG, Retrieval, Retrieval-augmented Generation

Large language models (LLMs) are increasingly integrated into real-world personalized applications through retrieval-augmented generation (RAG) mechanisms to supplement their responses with domain-specific knowledge. However, the valuable and often proprietary nature of the knowledge bases used in RAG introduces the risk of unauthorized usage by adversaries. Existing methods that can be generalized as watermarking techniques to protect these knowledge bases typically involve poisoning or backdoor attacks. However, these methods require altering the LLM's results of verification samples, inevitably making these watermarks susceptible to anomaly detection and even introducing new security risks. To address these challenges, we propose \name{} for `harmless' copyright protection of knowledge bases. Instead of manipulating LLM's final output, \name{} implants distinct yet benign verification behaviors in the space of chain-of-thought (CoT) reasoning, maintaining the correctness of the final answer. Our method has three main stages: (1) Generating CoTs: For each verification question, we generate two `innocent' CoTs, including a target CoT for building watermark behaviors; (2) Optimizing Watermark Phrases and Target CoTs: Inspired by our theoretical analysis, we optimize them to minimize retrieval errors under the \emph{black-box} and \emph{text-only} setting of suspicious LLM, ensuring that only watermarked verification queries can retrieve their correspondingly target CoTs contained in the knowledge base; (3) Ownership Verification: We exploit a pairwise Wilcoxon test to verify whether a suspicious LLM is augmented with the protected knowledge base by comparing its responses to watermarked and benign verification queries. Our experiments on diverse benchmarks demonstrate that \name{} effectively protects knowledge bases and its resistance to adaptive attacks.
Crypto Miner Attack: GPU Remote Code Execution Attacks
Authors: Ariel Szabo, Uzy Hadad
Date: 2025-02-09 00:00:00
Tasks: CPU, GPU

Remote Code Execution (RCE) exploits pose a significant threat to AI and ML systems, particularly in GPU-accelerated environments where the computational power of GPUs can be misused for malicious purposes. This paper focuses on RCE attacks leveraging deserialization vulnerabilities and custom layers, such as TensorFlow Lambda layers, which are often overlooked due to the complexity of monitoring GPU workloads. These vulnerabilities enable attackers to execute arbitrary code, blending malicious activity seamlessly into expected model behavior and exploiting GPUs for unauthorized tasks such as cryptocurrency mining. Unlike traditional CPU-based attacks, the parallel processing nature of GPUs and their high resource utilization make runtime detection exceptionally challenging. In this work, we provide a comprehensive examination of RCE exploits targeting GPUs, demonstrating an attack that utilizes these vulnerabilities to deploy a crypto miner on a GPU. We highlight the technical intricacies of such attacks, emphasize their potential for significant financial and computational costs, and propose strategies for mitigation. By shedding light on this underexplored attack vector, we aim to raise awareness and encourage the adoption of robust security measures in GPU-driven AI and ML systems, with an emphasis on static and model scanning as an easier way to detect exploits.
Injecting Universal Jailbreak Backdoors into LLMs in Minutes
Authors: Zhuowei Chen, Qiannan Zhang, Shichao Pei
Date: 2025-02-09 00:00:00
Tasks: Model Editing

Jailbreak backdoor attacks on LLMs have garnered attention for their effectiveness and stealth. However, existing methods rely on the crafting of poisoned datasets and the time-consuming process of fine-tuning. In this work, we propose JailbreakEdit, a novel jailbreak backdoor injection method that exploits model editing techniques to inject a universal jailbreak backdoor into safety-aligned LLMs with minimal intervention in minutes. JailbreakEdit integrates a multi-node target estimation to estimate the jailbreak space, thus creating shortcuts from the backdoor to this estimated jailbreak space that induce jailbreak actions. Our attack effectively shifts the models' attention by attaching strong semantics to the backdoor, enabling it to bypass internal safety mechanisms. Experimental results show that JailbreakEdit achieves a high jailbreak success rate on jailbreak prompts while preserving generation quality, and safe performance on normal queries. Our findings underscore the effectiveness, stealthiness, and explainability of JailbreakEdit, emphasizing the need for more advanced defense mechanisms in LLMs.
Decoding Complexity: Intelligent Pattern Exploration with CHPDA (Context Aware Hybrid Pattern Detection Algorithm)
Authors: Lokesh Koli, Shubham Kalra, Karanpreet Singh
Date: 2025-02-09 00:00:00
Tasks: Benchmarking, CPU

Detecting sensitive data such as Personally Identifiable Information (PII) and Protected Health Information (PHI) is critical for data security platforms. This study evaluates regex-based pattern matching algorithms and exact-match search techniques to optimize detection speed, accuracy, and scalability. Our benchmarking results indicate that Google RE2 provides the best balance of speed (10-15 ms/MB), memory efficiency (8-16 MB), and accuracy (99.5%) among regex engines, outperforming PCRE while maintaining broader hardware compatibility than Hyperscan. For exact matching, Aho-Corasick demonstrated superior performance (8 ms/MB) and scalability for large datasets. Performance analysis revealed that regex processing time scales linearly with dataset size and pattern complexity. A hybrid AI + Regex approach achieved the highest F1 score (91. 6%) by improving recall and minimizing false positives. Device benchmarking confirmed that our solution maintains efficient CPU and memory usage on both high-performance and mid-range systems. Despite its effectiveness, challenges remain, such as limited multilingual support and the need for regular pattern updates. Future work should focus on expanding language coverage, integrating data security and privacy management (DSPM) with data loss prevention (DLP) tools, and enhancing regulatory compliance for broader global adoption.
CryptoX : Compositional Reasoning Evaluation of Large Language Models
Authors: Jiajun Shi, Chaoren Wei, Liqun Yang, Zekun Moore Wang, Chenghao Yang, Ge Zhang, Stephen Huang, Tao Peng, Jian Yang, Zhoufutu Wen
Date: 2025-02-08 00:00:00

The compositional reasoning capacity has long been regarded as critical to the generalization and intelligence emergence of large language models LLMs. However, despite numerous reasoning-related benchmarks, the compositional reasoning capacity of LLMs is rarely studied or quantified in the existing benchmarks. In this paper, we introduce CryptoX, an evaluation framework that, for the first time, combines existing benchmarks and cryptographic, to quantify the compositional reasoning capacity of LLMs. Building upon CryptoX, we construct CryptoBench, which integrates these principles into several benchmarks for systematic evaluation. We conduct detailed experiments on widely used open-source and closed-source LLMs using CryptoBench, revealing a huge gap between open-source and closed-source LLMs. We further conduct thorough mechanical interpretability experiments to reveal the inner mechanism of LLMs' compositional reasoning, involving subproblem decomposition, subproblem inference, and summarizing subproblem conclusions. Through analysis based on CryptoBench, we highlight the value of independently studying compositional reasoning and emphasize the need to enhance the compositional reasoning capabilities of LLMs.
Culture is Not Trivia: Sociocultural Theory for Cultural NLP
Authors: Naitian Zhou, David Bamman, Isaac L. Bleaman
Date: 2025-02-17 00:00:00

The field of cultural NLP has recently experienced rapid growth, driven by a pressing need to ensure that language technologies are effective and safe across a pluralistic user base. This work has largely progressed without a shared conception of culture, instead choosing to rely on a wide array of cultural proxies. However, this leads to a number of recurring limitations: coarse national boundaries fail to capture nuanced differences that lay within them, limited coverage restricts datasets to only a subset of usually highly-represented cultures, and a lack of dynamicity results in static cultural benchmarks that do not change as culture evolves. In this position paper, we argue that these methodological limitations are symptomatic of a theoretical gap. We draw on a well-developed theory of culture from sociocultural linguistics to fill this gap by 1) demonstrating in a case study how it can clarify methodological constraints and affordances, 2) offering theoretically-motivated paths forward to achieving cultural competence, and 3) arguing that localization is a more useful framing for the goals of much current work in cultural NLP.
Investigating the importance of social vulnerability in opioid-related mortality across the United States
Authors: Andrew Deas, Adam Spannaus, Dakotah D. Maguire, Jodie Trafton, Anuj J. Kapadia, Vasileios Maroulas
Date: 2024-12-03 00:00:00
Tasks: Feature Importance

The opioid crisis remains a critical public health challenge in the United States. Despite national efforts to reduce opioid prescribing rates by nearly 45\% between 2011 and 2021, opioid overdose deaths more than tripled during this same period. This alarming trend reflects a major shift in the crisis, with illegal opioids now driving the majority of overdose deaths instead of prescription opioids. Although much attention has been given to supply-side factors fueling this transition, the underlying socioeconomic conditions that perpetuate and exacerbate opioid misuse remain less understood. Moreover, the COVID-19 pandemic intensified the opioid crisis through widespread social isolation and record-high unemployment; consequently, understanding the socioeconomic drivers of this epidemic has become even more crucial in recent years. To address this need, our study examines the correlation between opioid-related mortality and thirteen components of the Social Vulnerability Index (SVI). Leveraging a nationwide county-level dataset spanning consecutive years from 2010 to 2022, this study integrates empirical insights from exploratory data analysis with feature importance metrics derived from machine learning models. Our findings highlight critical social factors strongly correlated with opioid-related mortality, emphasizing their potential roles in worsening the epidemic when their levels are high and mitigating it when their levels are low.
Machine Learning Should Maximize Welfare, but Not by (Only) Maximizing Accuracy
Authors: Nir Rosenfeld, Haifeng Xu
Date: 2025-02-17 00:00:00

Decades of research in machine learning have given us powerful tools for making accurate predictions. This has made such tools appealing for use in social settings and on human inputs. Yet despite a lack of justification for why the generic approach of accuracy maximization can or should improve our collective well-being -- and mounting evidence of likely adverse outcomes -- it remains the widespread default. This position paper asserts that for machine learning to become socially beneficial, it must be embedded within a broader economic framework that explicitly aims to maximize social welfare. The field of welfare economics asks: how should we allocate limited resources among self-interested agents to maximize overall benefits? We contend that this perspective applies to many contemporary applications of machine learning in social contexts, and advocate for its adoption. Rather than disposing of prediction, we propose to leverage this forte of machine learning towards welfare maximization. We demonstrate this idea by portraying a conceptual framework that gradually transitions from accuracy maximization (with awareness to welfare) to welfare maximization (via accurate prediction). We detail applications and use-cases for which this framework can be effective, identify technical challenges and practical opportunities, and highlight future avenues worth pursuing.
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
Authors: Hao Zhou, Wenge Rong, Jianfei Zhang, Qing Sun, Yuanxin Ouyang, Zhang Xiong
Date: 2025-02-17 00:00:00
Tasks: Knowledge Tracing, Text Generation

Knowledge Tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive modeling on the sequence of former exercises has been proven effective for this task. One of the primary challenges in autoregressive modeling for Knowledge Tracing is effectively representing the anterior (pre-response) and posterior (post-response) states of learners across exercises. Existing methods often employ complex model architectures to update learner states using question and response records. In this study, we propose a novel perspective on knowledge tracing task by treating it as a generative process, consistent with the principles of autoregressive models. We demonstrate that knowledge states can be directly represented through autoregressive encodings on a question-response alternate sequence, where model generate the most probable representation in hidden state space by analyzing history interactions. This approach underpins our framework, termed Alternate Autoregressive Knowledge Tracing (AAKT). Additionally, we incorporate supplementary educational information, such as question-related skills, into our framework through an auxiliary task, and include extra exercise details, like response time, as additional inputs. Our proposed framework is implemented using advanced autoregressive technologies from Natural Language Generation (NLG) for both training and prediction. Empirical evaluations on four real-world KT datasets indicate that AAKT consistently outperforms all baseline models in terms of AUC, ACC, and RMSE. Furthermore, extensive ablation studies and visualized analysis validate the effectiveness of key components in AAKT.
Exploring LLM-based Student Simulation for Metacognitive Cultivation
Authors: Haoxuan Li, Jifan Yu, Xin Cong, Yang Dang, Daniel Zhang-li, Yisi Zhan, Huiqin Liu, Zhiyuan Liu
Date: 2025-02-17 00:00:00

Metacognitive education plays a crucial role in cultivating students' self-regulation and reflective thinking, providing essential support for those with learning difficulties through academic advising. Simulating students with insufficient learning capabilities using large language models offers a promising approach to refining pedagogical methods without ethical concerns. However, existing simulations often fail to authentically represent students' learning struggles and face challenges in evaluation due to the lack of reliable metrics and ethical constraints in data collection. To address these issues, we propose a pipeline for automatically generating and filtering high-quality simulated student agents. Our approach leverages a two-round automated scoring system validated by human experts and employs a score propagation module to obtain more consistent scores across the student graph. Experimental results demonstrate that our pipeline efficiently identifies high-quality student agents, and we discuss the traits that influence the simulation's effectiveness. By simulating students with varying degrees of learning difficulties, our work paves the way for broader applications in personalized learning and educational assessment.
Toward Metaphor-Fluid Conversation Design for Voice User Interfaces
Authors: Smit Desai, Jessie Chin, Dakuo Wang, Benjamin Cowan, Michael Twidale
Date: 2025-02-17 00:00:00

Metaphors play a critical role in shaping user experiences with Voice User Interfaces (VUIs), yet existing designs often rely on static, human-centric metaphors that fail to adapt to diverse contexts and user needs. This paper introduces Metaphor-Fluid Design, a novel approach that dynamically adjusts metaphorical representations based on conversational use-contexts. We compare this approach to a Default VUI, which characterizes the present implementation of commercial VUIs commonly designed around the persona of an assistant, offering a uniform interaction style across contexts. In Study 1 (N=130), metaphors were mapped to four key use-contexts-commands, information seeking, sociality, and error recovery-along the dimensions of formality and hierarchy, revealing distinct preferences for task-specific metaphorical designs. Study 2 (N=91) evaluates a Metaphor-Fluid VUI against a Default VUI, showing that the Metaphor-Fluid VUI enhances perceived intention to adopt, enjoyment, and likability by aligning better with user expectations for different contexts. However, individual differences in metaphor preferences highlight the need for personalization. These findings challenge the one-size-fits-all paradigm of VUI design and demonstrate the potential of Metaphor-Fluid Design to create more adaptive and engaging human-AI interactions.