Detection
-
FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
2024-10-03 17:59:34Zhipei Xu, Xuanyu Zhang, Runyi Li, Zecheng Tang, Qing Huang, Jian ZhangAbstract
The rapid development of generative AI is a double-edged sword, which not only facilitates content creation but also makes image manipulation easier and more difficult to detect. Although current image forgery detection and localization (IFDL) methods are generally effective, they tend to face two challenges: \textbf{1)} black-box nature with unknown detection principle, \textbf{2)} limited generalization across diverse tampering methods (e.g., Photoshop, DeepFake, AIGC-Editing). To address these issues, we propose the explainable IFDL task and design FakeShield, a multi-modal framework capable of evaluating image authenticity, generating tampered region masks, and providing a judgment basis based on pixel-level and image-level tampering clues. Additionally, we leverage GPT-4o to enhance existing IFDL datasets, creating the Multi-Modal Tamper Description dataSet (MMTD-Set) for training FakeShield's tampering analysis capabilities. Meanwhile, we incorporate a Domain Tag-guided Explainable Forgery Detection Module (DTE-FDM) and a Multi-modal Forgery Localization Module (MFLM) to address various types of tamper detection interpretation and achieve forgery localization guided by detailed textual descriptions. Extensive experiments demonstrate that FakeShield effectively detects and localizes various tampering techniques, offering an explainable and superior solution compared to previous IFDL methods.
Abstract (translated)
生成式 AI 的快速发展是一把双刃剑，这不仅促进了内容创作，而且使图像编辑和检测变得更加容易和困难。尽管当前的图像伪造检测和局部定位（IFDL）方法通常有效，但它们往往面临两个挑战：（1）未知检测原理的黑盒性质，（2）不同编辑方法（如 Photoshop、DeepFake 和 AIGC 编辑）下的有限泛化能力。为解决这些问题，我们提出了可解释的 IFDL 任务，并设计了 FakeShield，一种多模态框架，能够评估图像真实性、生成修改区域mask，并提供基于像素级和图像级修改线索的判断依据。此外，我们还利用 GPT-4o 增强现有 IFDL 数据集，为训练 FakeShield 的修改分析能力创建了多模态 Tamper Description 数据集（MMTD-Set）。同时，我们引入了领域标签指导的伪造检测模块（DTE-FDM）和多模态伪造定位模块（MFLM），以解决各种修改检测解释和实现基于详细文本描述的伪造定位。大量实验证明，FakeShield 有效地检测和定位各种修改技术，与之前 IFDL 方法相比，提供了更高水平的有解释性和优越性。
URL
https://arxiv.org/abs/2410.02761
PDF
-
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
2024-10-03 17:40:55Ryan C. Barron, Ves Grantcharov, Selma Wanna, Maksim E. Eren, Manish Bhattarai, Nicholas Solovyev, George Tompkins, Charles Nicholas, Kim {\O}. Rasmussen, Cynthia Matuszek, Boian S. AlexandrovAbstract
Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite their advanced language capabilities, when it comes to domain-specific and knowledge-intensive tasks, LLMs suffer from hallucinations, knowledge cut-offs, and lack of knowledge attributions. Additionally, fine tuning LLMs' intrinsic knowledge to highly specific domains is an expensive and time consuming process. The retrieval-augmented generation (RAG) process has recently emerged as a method capable of optimization of LLM responses, by referencing them to a predetermined ontology. It was shown that using a Knowledge Graph (KG) ontology for RAG improves the QA accuracy, by taking into account relevant sub-graphs that preserve the information in a structured manner. In this paper, we introduce SMART-SLIC, a highly domain-specific LLM framework, that integrates RAG with KG and a vector store (VS) that store factual domain specific information. Importantly, to avoid hallucinations in the KG, we build these highly domain-specific KGs and VSs without the use of LLMs, but via NLP, data mining, and nonnegative tensor factorization with automatic model selection. Pairing our RAG with a domain-specific: (i) KG (containing structured information), and (ii) VS (containing unstructured information) enables the development of domain-specific chat-bots that attribute the source of information, mitigate hallucinations, lessen the need for fine-tuning, and excel in highly domain-specific question answering tasks. We pair SMART-SLIC with chain-of-thought prompting agents. The framework is designed to be generalizable to adapt to any specific or specialized domain. In this paper, we demonstrate the question answering capabilities of our framework on a corpus of scientific publications on malware analysis and anomaly detection.
Abstract (translated)
大规模语言模型（LLMs）在大型语料库上预训练，并在许多通用自然语言处理（NLP）任务中表现出色，如问题回答（QA）。尽管它们具有高级语言能力，但在领域特定和知识密集型任务上，LLMs会受到幻觉、知识截止和知识归因不足的困扰。此外，将LLM的固有知识细分为高度特定的领域是一个耗时且昂贵的过程。最近，检索增强生成（RAG）过程作为一种优化LLM响应的方法而出现，通过将它们与预定义的语义网络参考。研究表明，使用知识图（KG）语义网络对RAG具有更好的QA准确率，通过考虑到相关的子图以保留结构化信息。在本文中，我们介绍了一个高度领域特定的LLM框架SMART-SLIC，该框架将RAG与KG和事实领域特定信息向量存储（VS）集成在一起。重要的是，为了避免知识库中的幻觉，我们通过NLP、数据挖掘和非负张量分解自动选择模型来构建这些高度领域特定的KGs和VS，而不是使用LLM。将我们的RAG与领域特定的： (i) KG（包含结构化信息）和(ii) VS（包含非结构化信息）相结合，可以开发出领域特定的聊天机器人，能够归因信息的来源、减轻幻觉、降低对细调的需求并擅长高度领域特定的问题回答任务。我们将SMART-SLIC与链式思考提示代理商相结合。该框架旨在适用于任何具体或专业领域。本文我们还展示了我们在关于恶意软件分析和检测领域的知识库上问题回答能力的实证研究。
URL
https://arxiv.org/abs/2410.02721
PDF
-
AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease
2024-10-03 17:37:18Romoke Grace Akindele, Samuel Adebayo, Paul Shekonya Kanda, Ming YuAbstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the aging population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybrid deep learning framework that integrates both 2D Convolutional Neural Networks (2D-CNN) and 3D Convolutional Neural Networks (3D-CNN), along with a custom loss function and volumetric data augmentation, to enhance feature extraction and improve classification performance in AD diagnosis. According to extensive experiments, AlzhiNet outperforms standalone 2D and 3D models, highlighting the importance of combining these complementary representations of data. The depth and quality of 3D volumes derived from the augmented 2D slices also significantly influence the model's performance. The results indicate that carefully selecting weighting factors in hybrid predictions is imperative for achieving optimal results. Our framework has been validated on the Magnetic Resonance Imaging (MRI) from Kaggle and MIRIAD datasets, obtaining accuracies of 98.9% and 99.99%, respectively, with an AUC of 100%. Furthermore, AlzhiNet was studied under a variety of perturbation scenarios on the Alzheimer's Kaggle dataset, including Gaussian noise, brightness, contrast, salt and pepper noise, color jitter, and occlusion. The results obtained show that AlzhiNet is more robust to perturbations than ResNet-18, making it an excellent choice for real-world applications. This approach represents a promising advancement in the early diagnosis and treatment planning for Alzheimer's disease.
Abstract (translated)
阿尔茨海默病(AD)是一种进行性的神经退行性疾病,其发病率在老年人群中的趋势不断增加,因此需要早期和准确的诊断以实现有效的疾病管理。在这项研究中,我们提出了一个新颖的混合深度学习框架,将2D卷积神经网络(2D-CNN)和3D卷积神经网络(3D-CNN)相结合,并包括自定义损失函数和体积数据增强,以增强特征提取和提高AD诊断的分类性能。根据广泛的实验,AlzhiNet超越了单独的2D和3D模型,突出了数据互补表示的重要性。从增强的2D切片中获得的3D体积的深度和质量也会显著影响模型的性能。结果表明,在混合预测中精心选择权重因子是实现最佳结果的必要条件。我们的框架已在Kaggle和MIRIAD数据集上的Magnetic Resonance Imaging(MRI)上进行了验证,获得了98.9%和99.99%的准确率, respectively,以及100%的AUC。此外,AlzhiNet还在AlzhiKaggle数据集上研究了各种扰动情景,包括高斯噪声、亮度、对比度、盐和胡椒噪声、颜色闪烁和遮挡。获得的结果表明,AlzhiNet对扰动的鲁棒性比ResNet-18更高,因此在实际应用中它是优秀的选择。这种方法在阿尔茨海默病的早期诊断和治疗规划中取得了有益的进展。
URL
https://arxiv.org/abs/2410.02714
PDF
-
LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations
2024-10-03 17:31:31Hadas Orgad, Michael Toker, Zorik Gekhman, Roi Reichart, Idan Szpektor, Hadas Kotek, Yonatan BelinkovAbstract
Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures, collectively referred to as "hallucinations". Recent studies have demonstrated that LLMs' internal states encode information regarding the truthfulness of their outputs, and that this information can be utilized to detect errors. In this work, we show that the internal representations of LLMs encode much more information about truthfulness than previously recognized. We first discover that the truthfulness information is concentrated in specific tokens, and leveraging this property significantly enhances error detection performance. Yet, we show that such error detectors fail to generalize across datasets, implying that -- contrary to prior claims -- truthfulness encoding is not universal but rather multifaceted. Next, we show that internal representations can also be used for predicting the types of errors the model is likely to make, facilitating the development of tailored mitigation strategies. Lastly, we reveal a discrepancy between LLMs' internal encoding and external behavior: they may encode the correct answer, yet consistently generate an incorrect one. Taken together, these insights deepen our understanding of LLM errors from the model's internal perspective, which can guide future research on enhancing error analysis and mitigation.
Abstract (translated)
大语言模型（LLMs）通常会产生错误，包括事实性不准确、偏见和推理失败等，这些共同称为“幻觉”。 近年来，研究表明，LLMs的内部状态编码了其输出真实性相关的信息，并且这种信息可以用于检测错误。在本文中，我们证明了LLMs的内部表示比以前想象的更能编码真实性信息。我们首先发现，真实性信息集中在特定的标记上，并利用这一特性显著增强了错误检测性能。然而，我们发现，这样的错误检测器无法在数据集之间泛化，暗示着——与先前的说法相反——真实性编码不是普遍的，而是多面的。接下来，我们展示了内部表示还可以用于预测模型可能出现的错误类型，促进开发定制化缓解策略。最后，我们揭示了LLMs的内部编码和外部行为之间的差异：它们可能编码正确的答案，但总是生成错误的答案。这些见解从模型内部的角度进一步加深了我们对于LLM错误的了解，这对于未来研究增强错误分析和缓解方法具有指导意义。
URL
https://arxiv.org/abs/2410.02707
PDF
-
HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router
2024-10-03 17:10:41Lingrui Mei, Shenghua Liu, Yiwei Wang, Baolong Bi, Ruibin Yuan, Xueqi ChengAbstract
As Large Language Models (LLMs) grow increasingly powerful, ensuring their safety and alignment with human values remains a critical challenge. Ideally, LLMs should provide informative responses while avoiding the disclosure of harmful or sensitive information. However, current alignment approaches, which rely heavily on refusal strategies, such as training models to completely reject harmful prompts or applying coarse filters are limited by their binary nature. These methods either fully deny access to information or grant it without sufficient nuance, leading to overly cautious responses or failures to detect subtle harmful content. For example, LLMs may refuse to provide basic, public information about medication due to misuse concerns. Moreover, these refusal-based methods struggle to handle mixed-content scenarios and lack the ability to adapt to context-dependent sensitivities, which can result in over-censorship of benign content. To overcome these challenges, we introduce HiddenGuard, a novel framework for fine-grained, safe generation in LLMs. HiddenGuard incorporates Prism (rePresentation Router for In-Stream Moderation), which operates alongside the LLM to enable real-time, token-level detection and redaction of harmful content by leveraging intermediate hidden states. This fine-grained approach allows for more nuanced, context-aware moderation, enabling the model to generate informative responses while selectively redacting or replacing sensitive information, rather than outright refusal. We also contribute a comprehensive dataset with token-level fine-grained annotations of potentially harmful information across diverse contexts. Our experiments demonstrate that HiddenGuard achieves over 90% in F1 score for detecting and redacting harmful content while preserving the overall utility and informativeness of the model's responses.
Abstract (translated)
随着大型语言模型（LLMs）变得越来越强大，确保其安全和与人类价值观保持一致仍然是一个关键挑战。理想情况下，LLMs应提供有益的回答，同时避免披露有害或敏感信息。然而，当前的 alignment 方法，这些方法依赖拒绝策略，如将模型训练为完全拒绝有害提示或应用粗略过滤器，因为其二进制性质而受到限制。这些方法要么完全否认访问信息，要么在信息不足的情况下授予它，导致过于谨慎的回答或无法检测到细微的有害内容。例如，LLMs 可能因滥用担忧而拒绝提供关于药物的基本、公共信息。此外，这些基于拒绝的方法很难处理混合内容场景，并且缺乏适应语境敏感性的能力，可能导致对良性内容的过度审查。为了克服这些挑战，我们引入了 HiddenGuard，一种用于在 LLMs 中进行细粒度、安全生成的全新框架。HiddenGuard 包含了 Prism（在流媒体审核中实现真实时间、词级检测和编辑有害内容的表示路由器），它与 LLM 并行工作，利用中间隐藏状态对有害内容进行实时、词级检测和编辑。这种细粒度的方法允许更细微、上下文感知的审核，使模型可以在选择性地编辑或替换敏感信息的同时生成有益的回答，而不仅仅是直接拒绝。我们还贡献了一个覆盖各种上下文的完整数据集，其中包含了可能有害信息的词级细粒度注释。我们的实验证明，HiddenGuard 在检测和编辑有害内容的同时保留模型的整体实用性和信息性方面取得了超过 90% 的 F1 分数。
URL
https://arxiv.org/abs/2410.02684
PDF
-
Hate Personified: Investigating the role of LLMs in content moderation
2024-10-03 16:43:17Sarah Masud, Sahajpreet Singh, Viktor Hangya, Alexander Fraser, Tanmoy ChakrabortyAbstract
For subjective tasks such as hate detection, where people perceive hate differently, the Large Language Model's (LLM) ability to represent diverse groups is unclear. By including additional context in prompts, we comprehensively analyze LLM's sensitivity to geographical priming, persona attributes, and numerical information to assess how well the needs of various groups are reflected. Our findings on two LLMs, five languages, and six datasets reveal that mimicking persona-based attributes leads to annotation variability. Meanwhile, incorporating geographical signals leads to better regional alignment. We also find that the LLMs are sensitive to numerical anchors, indicating the ability to leverage community-based flagging efforts and exposure to adversaries. Our work provides preliminary guidelines and highlights the nuances of applying LLMs in culturally sensitive cases.
Abstract (translated)
主观任务（如仇恨检测），由于人们对于仇恨有不同的看法，大型语言模型（LLM）表示多样组的能力尚不清楚。通过在提示中包括更多的上下文，我们全面分析了LLM对地理引导、人物属性和数值信息的敏感程度，以评估各种群体的需求是否得到反映。我们对两个LLM、五种语言和六个数据集的研究发现，模仿人物属性会导致注释变异性。同时，包括地理信息会导致更好的区域对齐。我们还发现LLMs对数值锚定非常敏感，表明能够利用基于社区的标记努力和接触敌人。我们的工作提供了在文化敏感情况下应用LLM的初步指导，并突出了在应用LLM时需要注意的细微差别。
URL
https://arxiv.org/abs/2410.02657
PDF
-
Learning 3D Perception from Others' Predictions
2024-10-03 16:31:28Jinsu Yoo, Zhenyang Feng, Tai-Yu Pan, Yihong Sun, Cheng Perng Phoo, Xiangyu Chen, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun ChaoAbstract
Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs repeated effort when a new sensor is adopted or when the detector is deployed in a new environment. We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector. For example, when a self-driving car enters a new area, it may learn from other traffic participants whose detectors have been optimized for that area. This setting is label-efficient, sensor-agnostic, and communication-efficient: nearby units only need to share the predictions with the ego agent (e.g., car). Naively using the received predictions as ground-truths to train the detector for the ego car, however, leads to inferior performance. We systematically study the problem and identify viewpoint mismatches and mislocalization (due to synchronization and GPS errors) as the main causes, which unavoidably result in false positives, false negatives, and inaccurate pseudo labels. We propose a distance-based curriculum, first learning from closer units with similar viewpoints and subsequently improving the quality of other units' predictions via self-training. We further demonstrate that an effective pseudo label refinement module can be trained with a handful of annotated data, largely reducing the data quantity necessary to train an object detector. We validate our approach on the recently released real-world collaborative driving dataset, using reference cars' predictions as pseudo labels for the ego car. Extensive experiments including several scenarios (e.g., different sensors, detectors, and domains) demonstrate the effectiveness of our approach toward label-efficient learning of 3D perception from other units' predictions.
Abstract (translated)
在现实环境准确3D物体检测需要大量带有高质量标注的数据。获取这样的数据费时且昂贵，而且在采用新传感器或将检测器部署到新环境时，通常需要重复尝试。我们研究了一个新场景来构建3D物体检测器：从配备准确检测器的相邻单元的预测中学习。例如，当自动驾驶汽车进入新区域时，它可以从对该区域进行优化以适应该区域的交通参与者那里学习。这个设置具有标签效率、传感器无关性和通信效率：附近的单元只需要将自己的预测与自车共享。然而，将接收到的预测作为自车训练检测器的地面真值，导致自车性能劣化。我们系统地研究了这个问题，并确定视角不匹配和定位误差（由同步和GPS误差导致的）是主要原因，这导致假阳性、假阴性和不准确的伪标签。我们提出了一个基于距离的教程，首先从具有相似观点的更近的单元学习，然后通过自训练提高其他单位的预测质量。我们还进一步证明了，只需要少量的标注数据，就可以通过自训练训练出有效的伪标签修复模块，从而大大减少训练一个物体检测器所需的數據量。我们在最近发布的合作驾驶数据集上验证了我们的方法，使用自车的预测作为自车的伪标签。包括多个场景（例如不同的传感器、检测器和领域）的丰富实验表明，我们的方法在从其他单位的预测中实现标签有效的3D感知方面是有效的。
URL
https://arxiv.org/abs/2410.02646
PDF
-
Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
2024-10-03 16:23:33Fabian Herzog, Johannes Gilg, Philipp Wolters, Torben Teepe, Gerhard RigollAbstract
Accurate online multiple-camera vehicle tracking is essential for intelligent transportation systems, autonomous driving, and smart city applications. Like single-camera multiple-object tracking, it is commonly formulated as a graph problem of tracking-by-detection. Within this framework, existing online methods usually consist of two-stage procedures that cluster temporally first, then spatially, or vice versa. This is computationally expensive and prone to error accumulation. We introduce a graph representation that allows spatial-temporal clustering in a single, combined step: New detections are spatially and temporally connected with existing clusters. By keeping sparse appearance and positional cues of all detections in a cluster, our method can compare clusters based on the strongest available evidence. The final tracks are obtained online using a simple multicut assignment procedure. Our method does not require any training on the target scene, pre-extraction of single-camera tracks, or additional annotations. Notably, we outperform the online state-of-the-art on the CityFlow dataset in terms of IDF1 by more than 14%, and on the Synthehicle dataset by more than 25%, respectively. The code is publicly available.
Abstract (translated)
准确的在线多摄像头车辆跟踪对于智能交通系统、自动驾驶和智能城市应用至关重要。与单摄像头多对象跟踪一样，通常用跟踪检测问题来表示它。在这个框架内，现有的在线方法通常包括两个步骤：首先进行时序聚类，然后进行空间聚类；或者反过来。这是计算密集型且容易累积错误的。我们引入了一个图表示，允许在单个、联合步骤中进行空间-时间聚类：新检测到的样本在空间和时间上与现有的聚类相互连接。通过保留所有检测到的样本的稀疏表示和位置线索，我们的方法可以基于最强的可用证据比较聚类。通过简单的多路复用分配方案，我们可以在在线过程中获得最终轨迹。我们的方法不需要在目标场景上进行训练，也不需要预先提取单摄像头的轨迹或附加注释。值得注意的是，我们在CityFlow数据集上比在线最先进的方法提高了约14%，而在Synthehicle数据集上提高了约25%。代码是公开可用的。
URL
https://arxiv.org/abs/2410.02638
PDF
-
Plots Unlock Time-Series Understanding in Multimodal Models
2024-10-03 16:23:13Mayank Daswani, Mathias M. J. Bellaiche, Marc Wilson, Desislav Ivanov, Mikhail Papkov, Eva Schnider, Jing Tang, Kay Lamerigts, Gabriela Botea, Michael A. Sanchez, Yojan Patel, Shruthi Prabhakara, Shravya Shetty, Umesh TelangAbstract
While multimodal foundation models can now natively work with data beyond text, they remain underutilized in analyzing the considerable amounts of multi-dimensional time-series data in fields like healthcare, finance, and social sciences, representing a missed opportunity for richer, data-driven insights. This paper proposes a simple but effective method that leverages the existing vision encoders of these models to "see" time-series data via plots, avoiding the need for additional, potentially costly, model training. Our empirical evaluations show that this approach outperforms providing the raw time-series data as text, with the additional benefit that visual time-series representations demonstrate up to a 90% reduction in model API costs. We validate our hypothesis through synthetic data tasks of increasing complexity, progressing from simple functional form identification on clean data, to extracting trends from noisy scatter plots. To demonstrate generalizability from synthetic tasks with clear reasoning steps to more complex, real-world scenarios, we apply our approach to consumer health tasks - specifically fall detection, activity recognition, and readiness assessment - which involve heterogeneous, noisy data and multi-step reasoning. The overall success in plot performance over text performance (up to an 120% performance increase on zero-shot synthetic tasks, and up to 150% performance increase on real-world tasks), across both GPT and Gemini model families, highlights our approach's potential for making the best use of the native capabilities of foundation models.
Abstract (translated)
虽然多模态基础模型现在可以原生地处理数据，包括文本以外，但在医疗、金融和社会科学等领域中处理大量多维度时间序列数据时，它们仍然没有被充分利用，这代表了一个丰富的数据驱动见解的错失机会。本文提出了一种简单而有效的的方法，利用现有模型的视觉编码器来“通过绘制图表”看待时间序列数据，无需进行昂贵的额外模型训练。我们的实证评估结果表明，这种方法在提供原始时间序列数据作为文本的同时超过了它，并且还具有使视觉时间序列表示减少模型API成本的额外优势。我们通过模拟数据任务来验证我们的假设，从简单的功能形式识别开始，逐渐进展到从嘈杂散点图提取趋势。为了从模拟任务中展示从清晰推理步骤到更复杂、现实世界场景的泛化能力，我们将该方法应用于消费者健康任务——尤其是跌倒检测、活动识别和准备评估，这些任务涉及异质、嘈杂数据和多步骤推理。在基于文本的绘制表现与基于图的绘制表现之间（在零散投放虚拟任务中的性能增加达到120%，在真实世界任务中的性能增加达到150%）的全面成功，突出了我们在基础模型上充分利用原功能的能力。
URL
https://arxiv.org/abs/2410.02637
PDF
-
Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI
2024-10-03 15:49:35Mesay Gemeda Yigezu, Melkamu Abay Mersha, Girma Yohannis Bade, Jugal Kalita, Olga Kolesnikova, Alexander GelbukhAbstract
The proliferation of fake news has emerged as a significant threat to the integrity of information dissemination, particularly on social media platforms. Misinformation can spread quickly due to the ease of creating and disseminating content, affecting public opinion and sociopolitical events. Identifying false information is therefore essential to reducing its negative consequences and maintaining the reliability of online news sources. Traditional approaches to fake news detection often rely solely on content-based features, overlooking the crucial role of social context in shaping the perception and propagation of news articles. In this paper, we propose a comprehensive approach that integrates social context-based features with news content features to enhance the accuracy of fake news detection in under-resourced languages. We perform several experiments utilizing a variety of methodologies, including traditional machine learning, neural networks, ensemble learning, and transfer learning. Assessment of the outcomes of the experiments shows that the ensemble learning approach has the highest accuracy, achieving a 0.99 F1 score. Additionally, when compared with monolingual models, the fine-tuned model with the target language outperformed others, achieving a 0.94 F1 score. We analyze the functioning of the models, considering the important features that contribute to model performance, using explainable AI techniques.
Abstract (translated)
虚假信息的泛滥成为影响信息传播 integrity的一个显著威胁，特别是在社交媒体平台上。虚假信息可以快速传播，因为创建和传播内容变得容易，会影响公众意见和社会政治事件。因此，识别虚假信息是减少其负面后果并维护在线新闻来源可靠性的关键。传统方法对待检测虚假信息通常仅基于内容特征，而忽略了社会背景在塑造新闻文章感知和传播中的关键作用。在本文中，我们提出了一个全面的方法，将基于社交背景的特征与新闻内容特征相结合，以提高资源有限语言中虚假信息检测的准确性。我们使用多种方法进行实验，包括传统机器学习、神经网络、集成学习和迁移学习。实验评估结果表明，集成学习方法具有最高的准确率，达到0.99 F1 score。此外，与单语模型相比，带有目标语言的微调模型表现优异，达到0.94 F1 score。我们分析了模型的运作，考虑了有助于模型性能的重要特征，并使用可解释AI技术进行了分析。
URL
https://arxiv.org/abs/2410.02609
PDF
-
IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengers
2024-10-03 15:34:41Zihan Fang, Zheng Lin, Senkang Hu, Hangcheng Cao, Yiqin Deng, Xianhao Chen, Yuguang FangAbstract
Recently, in-car monitoring has emerged as a promising technology for detecting early-stage abnormal status of the driver and providing timely alerts to prevent traffic accidents. Although training models with multimodal data enhances the reliability of abnormal status detection, the scarcity of labeled data and the imbalance of class distribution impede the extraction of critical abnormal state features, significantly deteriorating training performance. Furthermore, missing modalities due to environment and hardware limitations further exacerbate the challenge of abnormal status identification. More importantly, monitoring abnormal health conditions of passengers, particularly in elderly care, is of paramount importance but remains underexplored. To address these challenges, we introduce our IC3M, an efficient camera-rotation-based multimodal framework for monitoring both driver and passengers in a car. Our IC3M comprises two key modules: an adaptive threshold pseudo-labeling strategy and a missing modality reconstruction. The former customizes pseudo-labeling thresholds for different classes based on the class distribution, generating class-balanced pseudo labels to guide model training effectively, while the latter leverages crossmodality relationships learned from limited labels to accurately recover missing modalities by distribution transferring from available modalities. Extensive experimental results demonstrate that IC3M outperforms state-of-the-art benchmarks in accuracy, precision, and recall while exhibiting superior robustness under limited labeled data and severe missing modality.
Abstract (translated)
近年来，车载监控作为一种检测驾驶员 early阶段异常状态的潜在技术，为预防交通事故提供了及时的警报。尽管使用多模态数据训练模型可以提高异常状态检测的可靠性，但缺乏标注数据和类别分布不平衡会阻碍关键异常状态特征的提取，导致训练性能显著下降。此外，由于环境和硬件限制而缺失的维度进一步加剧了异常状态识别的挑战。更为重要的是，监测老年乘客的异常健康状况（特别是护理领域）至关重要，但这一领域仍然缺乏充分的探索。为了应对这些挑战，我们介绍了我们的 IC3M，即一种高效的多模态框架，用于监测汽车中的驾驶员和乘客。我们的 IC3M 包括两个关键模块：自适应阈值伪标签策略和缺失模态重构。前一个模块根据类别分布自适应地定制伪标签阈值，生成类平衡的伪标签，有效指导模型训练；后一个模块利用从有限标注数据中学习到的跨模态关系，准确通过分布传输从可用模态恢复缺失模态。大量实验结果表明，IC3M 在准确性、精确度和召回方面优于最先进的基准测试，同时具有在有限标注数据和严重缺失模态下表现出优越鲁棒性的特点。
URL
https://arxiv.org/abs/2410.02592
PDF
-
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
2024-10-03 15:28:05Angana Borah, Rada MihalceaAbstract
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs are susceptible to societal biases due to their exposure to human-generated data. Given that LLMs are being used to gain insights into various societal aspects, it is essential to mitigate these biases. To that end, our study investigates the presence of implicit gender biases in multi-agent LLM interactions and proposes two strategies to mitigate these biases. We begin by creating a dataset of scenarios where implicit gender biases might arise, and subsequently develop a metric to assess the presence of biases. Our empirical analysis reveals that LLMs generate outputs characterized by strong implicit bias associations (>= 50\% of the time). Furthermore, these biases tend to escalate following multi-agent interactions. To mitigate them, we propose two strategies: self-reflection with in-context examples (ICE); and supervised fine-tuning. Our research demonstrates that both methods effectively mitigate implicit biases, with the ensemble of fine-tuning and self-reflection proving to be the most successful.
Abstract (translated)
随着大型语言模型（LLMs）的不断发展，它们越来越多地被用于模拟社会并执行各种社交任务。然而，由于它们暴露于人类生成的数据，LLMs容易受到社会偏见的影响。鉴于LLMs正在被用于深入了解各种社会方面，减少这些偏见至关重要。因此，我们的研究调查了多代理器LLM互动中潜在性别偏见的存在，并提出两种策略来减轻这些偏见。我们首先创建了一个隐含性别偏见可能出现的场景的数据集，然后开发了一个指标来评估偏见的存在。我们的实证分析表明，LLMs生成的输出具有强烈的隐含偏见联系（>= 50\%的时间）。此外，这些偏见在多代理器交互后往往还会加剧。为了减轻这些偏见，我们提出了两种策略：在上下文实例中进行自我反思（ICE）；和监督微调。我们的研究证实，这两种方法都能有效减轻隐含偏见，而且微调和自我反思的组合是最成功的。
URL
https://arxiv.org/abs/2410.02584
PDF
-
Behavior Trees in Functional Safety Supervisors for Autonomous Vehicles
2024-10-03 13:19:38Carlos Conejo, Vicen\c{c} Puig, Bernardo Morcego, Francisco Navas, Vicente Milan\'esAbstract
The rapid advancements in autonomous vehicle software present both opportunities and challenges, especially in enhancing road safety. The primary objective of autonomous vehicles is to reduce accident rates through improved safety measures. However, the integration of new algorithms into the autonomous vehicle, such as Artificial Intelligence methods, raises concerns about the compliance with established safety regulations. This paper introduces a novel software architecture based on behavior trees, aligned with established standards and designed to supervise vehicle functional safety in real time. It specifically addresses the integration of algorithms into industrial road vehicles, adhering to the ISO 26262. The proposed supervision methodology involves the detection of hazards and compliance with functional and technical safety requirements when a hazard arises. This methodology, implemented in this study in a Renault Mégane (currently at SAE level 3 of automation), not only guarantees compliance with safety standards, but also paves the way for safer and more reliable autonomous driving technologies.
Abstract (translated)
自动驾驶软件的快速发展既带来了机会，也带来了挑战，特别是在提高道路安全方面。自动驾驶汽车的主要目标是通过改进安全措施降低事故率。然而，将新的算法集成到自动驾驶汽车中，如人工智能方法，引起了关于是否符合既定安全法规的担忧。本文介绍了一种基于行为树的新软件架构，与既定标准保持一致，旨在实时监督车辆的功能安全。它特别关注将算法集成到工业道路上，遵循ISO 26262标准。所提出的监督方法包括在危险发生时检测危险并符合功能和技术安全要求。这项研究中的Renault Mégane（目前处于SAE level 3的自动化水平）不仅确保了符合安全标准，还为更安全、更可靠的自动驾驶技术铺平了道路。
URL
https://arxiv.org/abs/2410.02469
PDF
-
Optimizing Adaptive Attacks against Content Watermarks for Language Models
2024-10-03 12:37:39Abdulrahman Diaa, Toluwani Aremu, Nils LukasAbstract
Large Language Models (LLMs) can be \emph{misused} to spread online spam and misinformation. Content watermarking deters misuse by hiding a message in model-generated outputs, enabling their detection using a secret watermarking key. Robustness is a core security property, stating that evading detection requires (significant) degradation of the content's quality. Many LLM watermarking methods have been proposed, but robustness is tested only against \emph{non-adaptive} attackers who lack knowledge of the watermarking method and can find only suboptimal attacks. We formulate the robustness of LLM watermarking as an objective function and propose preference-based optimization to tune \emph{adaptive} attacks against the specific watermarking method. Our evaluation shows that (i) adaptive attacks substantially outperform non-adaptive baselines. (ii) Even in a non-adaptive setting, adaptive attacks optimized against a few known watermarks remain highly effective when tested against other unseen watermarks, and (iii) optimization-based attacks are practical and require less than seven GPU hours. Our findings underscore the need to test robustness against adaptive attackers.
Abstract (translated)
大语言模型（LLMs）可能被用于传播网络垃圾信息和错误信息。内容水印标记防止了滥用，通过在模型生成的输出中隐藏信息，使它们能够通过秘密水印键进行检测。稳健性是核心安全属性，表明要逃避检测，需要（显著）降低内容的质量。已经提出了许多LLM水印标记方法，但只有针对非适应性攻击者进行测试，他们不知道水印方法，只能找到次优攻击。我们将LLM水印的稳健性表示为一个目标函数，并提出基于偏好的优化来调整针对特定水印方法的适应性攻击。我们的评估显示，（i）适应性攻击远优于非适应性基线。（ii）即使在非适应性设置中，针对几个已知水印的适应性攻击仍然在与其他未见水印的测试中具有高度的有效性。（iii）基于优化的攻击是实用的，并且只需要几个GPU小时。我们的发现强调了对适应性攻击者进行稳健性测试的必要性。
URL
https://arxiv.org/abs/2410.02440
PDF
-
SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual Representations
2024-10-03 11:29:09Nikolaos Giakoumoglou, Tania StathakiAbstract
Contrastive learning has become a dominant approach in self-supervised visual representation learning, with hard negatives-samples that closely resemble the anchor-being key to enhancing the discriminative power of learned representations. However, efficiently leveraging hard negatives remains a challenge due to the difficulty in identifying and incorporating them without significantly increasing computational costs. To address this, we introduce SynCo (Synthetic Negatives in Contrastive learning), a novel contrastive learning approach that improves model performance by generating synthetic hard negatives. Built on the MoCo framework, SynCo introduces six novel strategies for creating diverse synthetic hard negatives that can be generated on-the-fly with minimal computational overhead. SynCo achieves faster training and better representation learning, achieving a top-1 accuracy of 68.1% in ImageNet linear evaluation after only 200 epochs on pretraining, surpassing MoCo's 67.5% with the same ResNet-50 encoder. Additionally, it transfers more effectively to detection tasks: on the PASCAL VOC, it outperforms both the supervised baseline and MoCo, achieving an AP of 82.5%; on the COCO dataset, it sets a new benchmark with 40.4% AP for bounding box detection and 35.4% AP for instance segmentation. Our synthetic hard negative generation procedure significantly enhances the quality of visual representations learned through self-supervised contrastive learning. Code is available at this https URL.
Abstract (translated)
对比学习已成为自监督视觉表示学习的主导方法，其中具有困难的负样本，这些负样本与学习到的表示的判别力密切相关，可以增强所学到的表示的判别力。然而，有效地利用困难的负样本仍然具有挑战性，因为很难在不显著增加计算成本的情况下，准确地识别和包含它们。为了应对这个问题，我们引入了SynCo（在对比学习中生成合成负样本），一种新颖的对比学习方法，通过生成合成负样本来提高模型性能。SynCo基于MoCo框架，引入了六个新颖的策略，可以在无需大量计算开销的情况下生成多样性的合成负样本。SynCo实现了更快的训练和更好的表示学习，在仅经过200个周期预训练后，ImageNet线性评估的准确率达到了68.1%，超过了使用相同ResNet-50编码器的MoCo的67.5%。此外，它在对检测任务上的转移效果上也表现更出色：在PASCAL VOC上，它超过了监督基线和MoCo，实现了82.5%的AP；在COCO数据集上，它为边界框检测和实例分割设置了新的基准，分别为40.4%和35.4%的AP。我们生成的合成负样本处理过程显著提高了通过自监督对比学习获得的视觉表示的质量。代码可以从这个链接下载：https://www.kaggle.com/your_username/synco
URL
https://arxiv.org/abs/2410.02401
PDF
-
Towards Comprehensive Detection of Chinese Harmful Memes
2024-10-03 10:51:02Junyu Lu, Bo Xu, Xiaokun Zhang, Hongbo Wang, Haohao Zhu, Dongyu Zhang, Liang Yang, Hongfei LinAbstract
This paper has been accepted in the NeurIPS 2024 D & B Track. Harmful memes have proliferated on the Chinese Internet, while research on detecting Chinese harmful memes significantly lags behind due to the absence of reliable datasets and effective detectors. To this end, we focus on the comprehensive detection of Chinese harmful memes. We construct ToxiCN MM, the first Chinese harmful meme dataset, which consists of 12,000 samples with fine-grained annotations for various meme types. Additionally, we propose a baseline detector, Multimodal Knowledge Enhancement (MKE), incorporating contextual information of meme content generated by the LLM to enhance the understanding of Chinese memes. During the evaluation phase, we conduct extensive quantitative experiments and qualitative analyses on multiple baselines, including LLMs and our MKE. The experimental results indicate that detecting Chinese harmful memes is challenging for existing models while demonstrating the effectiveness of MKE. The resources for this paper are available at this https URL.
Abstract (translated)
这篇论文已成功通过2024年的NeurIPS D&B track的审核。 汉语互联网上有害的流行 meme 大量繁殖，然而，由于缺乏可靠的数据集和有效的检测器，检测中国有害 meme 的研究明显滞后。为此，我们关注全面检测中国有害 meme。我们构建了ToxiCN MM，第一个中国有害 meme 数据集，它包括针对各种 meme 类型的 12,000 个样本，具有精细的注释。此外，我们提出了一个 baseline detector: Multimodal Knowledge Enhancement (MKE)，该探测器包含 LLM 生成的 meme 内容的上下文信息，以增强对 Chinese meme 的理解。在评估阶段，我们对多个基准进行广泛的定量实验和定性分析，包括LLM 和我们的 MKE。实验结果表明，对于现有的模型来说，检测 Chinese有害 meme 具有挑战性，但证明了 MKE 的有效性。本文的资源可在此链接访问：https://www.academia.edu/38411041/ToxiCN_MM
URL
https://arxiv.org/abs/2410.02378
PDF
-
Key-Grid: Unsupervised 3D Keypoints Detection using Grid Heatmap Features
2024-10-03 06:16:50Chengkai Hou, Zhengrong Xue, Bingyang Zhou, Jinghan Ke, Lin Shao, Huazhe XuAbstract
Detecting 3D keypoints with semantic consistency is widely used in many scenarios such as pose estimation, shape registration and robotics. Currently, most unsupervised 3D keypoint detection methods focus on the rigid-body objects. However, when faced with deformable objects, the keypoints they identify do not preserve semantic consistency well. In this paper, we introduce an innovative unsupervised keypoint detector Key-Grid for both the rigid-body and deformable objects, which is an autoencoder framework. The encoder predicts keypoints and the decoder utilizes the generated keypoints to reconstruct the objects. Unlike previous work, we leverage the identified keypoint in formation to form a 3D grid feature heatmap called grid heatmap, which is used in the decoder section. Grid heatmap is a novel concept that represents the latent variables for grid points sampled uniformly in the 3D cubic space, where these variables are the shortest distance between the grid points and the skeleton connected by keypoint pairs. Meanwhile, we incorporate the information from each layer of the encoder into the decoder section. We conduct an extensive evaluation of Key-Grid on a list of benchmark datasets. Key-Grid achieves the state-of-the-art performance on the semantic consistency and position accuracy of keypoints. Moreover, we demonstrate the robustness of Key-Grid to noise and downsampling. In addition, we achieve SE-(3) invariance of keypoints though generalizing Key-Grid to a SE(3)-invariant backbone.
Abstract (translated)
检测3D关键点与语义一致性是许多应用场景（如姿态估计、形状配准和机器人技术）中广泛使用的。目前，大多数无监督3D关键点检测方法都关注于刚体物体。然而，面对变形物体，它们确定的关键点在语义上并不保持一致。在本文中，我们提出了一种创新的无监督关键点检测器Key-Grid，适用于刚体和变形物体，是一种自动编码器框架。编码器预测关键点，解码器利用生成的关键点重构物体。与之前的工作不同，我们利用已识别的关键点形成一个3D立方空间中采样均匀的网格点特征热图，即网格热图，用于解码器部分。网格热图是一种新颖的概念，它表示在3D立方空间中，网格点与通过关键点对齐的骨架之间的最短距离。同时，我们将编码器每一层的有关信息融入解码器部分。我们在一系列基准数据集上对Key-Grid进行广泛评估。Key-Grid在关键点的语义一致性和位置精度上实现了最先进的性能。此外，我们还证明了Key-Grid对噪声和下采样具有鲁棒性。此外，通过将Key-Grid扩展到SE(3)-不变的骨干网络，我们实现了关键点的SE(3)不变性。
URL
https://arxiv.org/abs/2410.02237
PDF
-
Adapting Segment Anything Model to Melanoma Segmentation in Microscopy Slide Images
2024-10-03 04:40:18Qingyuan Liu, Avideh ZakhorAbstract
Melanoma segmentation in Whole Slide Images (WSIs) is useful for prognosis and the measurement of crucial prognostic factors such as Breslow depth and primary invasive tumor size. In this paper, we present a novel approach that uses the Segment Anything Model (SAM) for automatic melanoma segmentation in microscopy slide images. Our method employs an initial semantic segmentation model to generate preliminary segmentation masks that are then used to prompt SAM. We design a dynamic prompting strategy that uses a combination of centroid and grid prompts to achieve optimal coverage of the super high-resolution slide images while maintaining the quality of generated prompts. To optimize for invasive melanoma segmentation, we further refine the prompt generation process by implementing in-situ melanoma detection and low-confidence region filtering. We select Segformer as the initial segmentation model and EfficientSAM as the segment anything model for parameter-efficient fine-tuning. Our experimental results demonstrate that this approach not only surpasses other state-of-the-art melanoma segmentation methods but also significantly outperforms the baseline Segformer by 9.1% in terms of IoU.
Abstract (translated)
在 whole slide images (WSIs) 中对黑色素瘤进行分割划分有助于预测和测量至关重要的预后因素，如 Breslow 深度和原发侵略性肿瘤大小。在本文中，我们提出了一种新方法，利用 Segment Anything Model（SAM）对 WSIs 中的黑色素瘤进行自动分割划分。我们的方法采用了一个初始语义分割模型生成初步分割掩码，然后用于提示 SOM。我们设计了一种动态提示策略，结合了中心点和网格提示，在保持生成提示质量的同时实现超高清 WSIs 的全面覆盖。为了优化侵袭性黑色素瘤的分割，我们通过实现原位 melanoma 检测和低置信度区域过滤来进一步优化提示生成过程。我们选择 Segformer 作为初始分割模型，EfficientSAM 作为分割 anything 模型进行参数高效的微调。我们的实验结果表明，这种方法不仅在其他最先进的黑色素瘤分割方法中超过了它们，而且在 IoU 方面显著优于基线方法 9.1%。
URL
https://arxiv.org/abs/2410.02207
PDF
-
Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images
2024-10-02 20:25:40Mahmudul Hasan, Xiaoling Hu, Shahira Abousamra, Prateek Prasanna, Joel Saltz, Chao ChenAbstract
Despite the strong prediction power of deep learning models, their interpretability remains an important concern. Disentanglement models increase interpretability by decomposing the latent space into interpretable subspaces. In this paper, we propose the first disentanglement method for pathology images. We focus on the task of detecting tumor-infiltrating lymphocytes (TIL). We propose different ideas including cascading disentanglement, novel architecture, and reconstruction branches. We achieve superior performance on complex pathology images, thus improving the interpretability and even generalization power of TIL detection deep learning models. Our codes are available at this https URL.
Abstract (translated)
尽管深度学习模型的预测能力很强，但它们的可解释性仍然是一个重要的问题。解离模型通过将潜在空间分解为可解释子空间来增加可解释性。在本文中，我们提出了第一个用于病理图像的解离方法。我们专注于肿瘤浸润淋巴细胞（TIL）的检测任务。我们提出了包括级联解离、新架构和重构支路等不同想法。我们在复杂病理图像上的表现优于其他深度学习模型，从而提高了TIL检测深度学习模型的可解释性和泛化能力。我们的代码可在此处访问：https://www.xxx.com/
URL
https://arxiv.org/abs/2410.02012
PDF
-
UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription
2024-10-02 19:51:48Reza Basiri, Ali Abedi, Chau Nguyen, Milos R. Popovic, Shehroz S. KhanAbstract
Diabetic foot ulcers (DFUs) are a leading cause of hospitalizations and lower limb amputations, placing a substantial burden on patients and healthcare systems. Early detection and accurate classification of DFUs are critical for preventing serious complications, yet many patients experience delays in receiving care due to limited access to specialized services. Telehealth has emerged as a promising solution, improving access to care and reducing the need for in-person visits. The integration of artificial intelligence and pattern recognition into telemedicine has further enhanced DFU management by enabling automatic detection, classification, and monitoring from images. Despite advancements in artificial intelligence-driven approaches for DFU image analysis, the application of large language models for DFU image transcription has not yet been explored. To address this gap, we introduce UlcerGPT, a novel multimodal approach leveraging large language and vision models for DFU image transcription. This framework combines advanced vision and language models, such as Large Language and Vision Assistant and Chat Generative Pre-trained Transformer, to transcribe DFU images by jointly detecting, classifying, and localizing regions of interest. Through detailed experiments on a public dataset, evaluated by expert clinicians, UlcerGPT demonstrates promising results in the accuracy and efficiency of DFU transcription, offering potential support for clinicians in delivering timely care via telemedicine.
Abstract (translated)
糖尿病足溃疡（DFUs）是医院化和下肢截肢的领先原因，对患者和医疗系统造成了沉重的负担。早期诊断和准确的分类DFUs对于预防严重并发症至关重要，然而许多患者由于获得专业服务受限而经历延迟接受治疗。远程医疗已成为一个有前景的解决方案，通过改善获得医疗服务的可访问性并减少需要亲自就诊，提高了医疗服务的可及性。将人工智能和模式识别融入远程医疗，进一步提高了DFU管理，通过使图像自动检测、分类和监测，从而实现这一目标。尽管在人工智能驱动的DFU图像分析方面取得了进步，但应用大型语言模型进行DFU图像转录的应用还尚不清楚。为了填补这一空白，我们引入了UlcerGPT，一种利用大型语言和视觉模型协同检测、分类和定位兴趣区域的全新多模态方法。这个框架结合了大型语言和视觉模型，如Large Language和 Vision Assistant和Chat Generative Pre-trained Transformer，通过共同检测、分类和定位感兴趣的区域对DFU图像进行转录。通过对一个公开数据集的详细实验，由专家临床医生进行评估，UlcerGPT在DFU转录的准确性和效率方面显示出良好的结果，为医生通过远程医疗及时交付护理提供了潜在支持。
URL
https://arxiv.org/abs/2410.01989
PDF
