Related papers: Automatically Detecting Online Deceptive Patterns in Real-time

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On the Internet, Nobody Knows You're an LLM Bot: Unmasking Web Agents with Multi-Layer Fingerprinting [1.4726407245786044]
Since 2023, a new class of bots has emerged: Web Agents.<n>They can automate complex tasks on the Web, going beyond traditional browser automation tools such as Selenium, Puppeteer, or Playwright.<n>It is becoming increasingly difficult for website administrators to detect and block these LLM-based bots.
arXiv Detail & Related papers (2026-06-29T10:51:13Z)
WebTestBench: Evaluating Computer-Use Agents towards End-to-End Automated Web Testing [57.7131457251794]
We introduce WebTestBench, a benchmark for evaluating end-to-end automated web testing.<n>We decompose the testing process into two cascaded sub-tasks, checklist generation and defect detection, and propose WebTester.<n>Our findings expose a substantial gap between current computer-use agent capabilities and industrial-grade deployment demands.
arXiv Detail & Related papers (2026-03-26T09:27:29Z)
AutoOEP -- A Multi-modal Framework for Online Exam Proctoring [1.6522310568442877]
This paper introduces AutoOEP (Automated Online Exam Proctoring), a comprehensive, multi-modal framework that leverages computer vision and machine learning to provide effective, automated proctoring.<n>The system utilizes a dual-camera setup to capture both a frontal view of the examinee and a side view of the workspace, minimizing blind spots.<n>The Hand Module employs a fine-tuned YOLOv11 model for detecting prohibited items (e.g., mobile phones, notes) and tracks hand proximity to these objects.
arXiv Detail & Related papers (2025-09-13T16:34:38Z)
Human-AI Collaborative Bot Detection in MMORPGs [1.6901808995670526]
In Massively Multiplayer Online Role-Playing Games (MMORPGs), auto-leveling bots exploit automated programs to level up characters at scale.<n>We present a novel framework for detecting auto-leveling bots by leveraging contrastive representation learning and clustering techniques.
arXiv Detail & Related papers (2025-08-28T09:17:35Z)
VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents [74.6761188527948]
Computer-Use Agents (CUAs) with full system access pose significant security and privacy risks.<n>We investigate Visual Prompt Injection (VPI) attacks, where malicious instructions are visually embedded within rendered user interfaces.<n>Our empirical study shows that current CUAs and BUAs can be deceived at rates of up to 51% and 100%, respectively, on certain platforms.
arXiv Detail & Related papers (2025-06-03T05:21:50Z)
CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
arXiv Detail & Related papers (2025-05-14T13:37:07Z)
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We propose a novel deep learning based end-to-end learning framework for prediction of exposure of PII in mobile packets.<n>The framework employs a pre-trained large language model (LLM) and an autoencoder to generate embedding of network packets.<n>We compare our proposed detection framework with other state-of-the-art works in detecting PII leaks from user's device.
arXiv Detail & Related papers (2025-02-13T06:43:46Z)
Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions [9.495142718502072]
Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising. Prior research heavily relied on automated web crawls, which inherently struggle to replicate the nuances of human-computer interactions. This paper presents a user study involving 30 participants over 10 weeks, capturing telemetry data from real browsing sessions across 3,000 top-ranked websites.
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AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials [53.376263056033046]
Existing approaches rely on expensive human annotation, making them unsustainable at scale.<n>We propose AgentTrek, a scalable data synthesis pipeline that generates web agent trajectories by leveraging publicly available tutorials.<n>Our fully automated approach significantly reduces data collection costs, achieving a cost of just $0.55 per high-quality trajectory without human annotators.
arXiv Detail & Related papers (2024-12-12T18:59:27Z)
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We propose a novel approach for object detection in dashcams using transformers. Our system is based on the state-of-the-art DEtection TRansformer (DETR) Our results show that the use of intelligent automation through transformers can significantly enhance the capabilities of dashcam systems.
arXiv Detail & Related papers (2024-08-28T14:08:24Z)
Detecting Deceptive Dark Patterns in E-commerce Platforms [0.0]
Dark patterns are deceptive user interfaces employed by e-commerce websites to manipulate user's behavior in a way that benefits the website, often unethically. Existing solutions include UIGuard, which uses computer vision and natural language processing, and approaches that categorize dark patterns based on detectability or utilize machine learning models trained on datasets. We propose combining web scraping techniques with fine-tuned BERT language models and generative capabilities to identify dark patterns, including outliers.
arXiv Detail & Related papers (2024-05-27T16:32:40Z)
Autosen: improving automatic wifi human sensing through cross-modal autoencoder [56.44764266426344]
WiFi human sensing is highly regarded for its low-cost and privacy advantages in recognizing human activities. Traditional cross-modal methods, aimed at enabling self-supervised learning without labeled data, struggle to extract meaningful features from amplitude-phase combinations. We introduce AutoSen, an innovative automatic WiFi sensing solution that departs from conventional approaches.
arXiv Detail & Related papers (2024-01-08T19:50:02Z)
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control [140.48218261864153]
We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control. Our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training.
arXiv Detail & Related papers (2023-07-28T21:18:02Z)
Can AI-Generated Text be Reliably Detected? [50.95804851595018]
Large Language Models (LLMs) perform impressively well in various applications.<n>The potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use.<n>We stress-test the robustness of these AI text detectors in the presence of an attacker.
arXiv Detail & Related papers (2023-03-17T17:53:19Z)
DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning [41.90094833178758]
DeepCAVE is an interactive framework to analyze and monitor state-of-the-art optimization procedures for AutoML easily and ad hoc. Our framework's modular and easy-to-extend nature provides users with automatically generated text, tables, and graphic visualizations.
arXiv Detail & Related papers (2022-06-07T12:59:39Z)
Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
arXiv Detail & Related papers (2021-12-08T14:12:24Z)
Automating Privilege Escalation with Deep Reinforcement Learning [71.87228372303453]
In this work, we exemplify the potential threat of malicious actors using deep reinforcement learning to train automated agents. We present an agent that uses a state-of-the-art reinforcement learning algorithm to perform local privilege escalation. Our agent is usable for generating realistic attack sensor data for training and evaluating intrusion detection systems.
arXiv Detail & Related papers (2021-10-04T12:20:46Z)
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection [55.12894776039135]
State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies. We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain. We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
arXiv Detail & Related papers (2021-03-26T01:18:11Z)
An Automated Machine Learning (AutoML) Method for Driving Distraction Detection Based on Lane-Keeping Performance [2.3951613028271397]
This study proposes a domain-specific automated machine learning (AutoML) to self-learn the optimal models to detect distraction. The proposed AutoGBM method is found to be reliable and promising to predict phone-related driving distractions. The purposed AutoGBM not only produces better performance with fewer features; but also provides data-driven insights about system design.
arXiv Detail & Related papers (2021-03-10T12:37:18Z)
Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
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arXiv Detail & Related papers (2020-12-15T03:03:38Z)
Artificial Intelligence Enabled Traffic Monitoring System [3.085453921856008]
This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs.
arXiv Detail & Related papers (2020-10-02T22:28:02Z)
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference [21.707911452679152]
We introduce an automated Bayesian inference framework, called AutoBayes, to optimize nuisance-invariant machine learning pipelines. We demonstrate a significant performance improvement with ensemble learning across explored graphical models.
arXiv Detail & Related papers (2020-07-02T17:06:26Z)
Detection of Novel Social Bots by Ensembles of Specialized Classifiers [60.63582690037839]
Malicious actors create inauthentic social media accounts controlled in part by algorithms, known as social bots, to disseminate misinformation and agitate online discussion. We show that different types of bots are characterized by different behavioral features. We propose a new supervised learning method that trains classifiers specialized for each class of bots and combines their decisions through the maximum rule.
arXiv Detail & Related papers (2020-06-11T22:59:59Z)

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