About

Habiba Farrukh is an Assistant Professor in the Department of Computer Science at UC Irvine's Donald Bren School of Information & Computer Sciences. Her research interests broadly lie in the areas of security, privacy, and mobile computing. She focuses on the advanced computing and sensing capabilities of mobile systems, which make them attractive targets for attackers, thereby threatening users' security and privacy. Farrukh's work combines system design, signal processing, computer vision, and machine learning methods to investigate these threats and develop methods for securing modern mobile and IoT systems while protecting user privacy. She works on designing practical solutions to address the security limitations of existing systems and emerging computing platforms. Her research also explores the impact of these limitations on developers and end users, aiming to propose secure and usable systems that address the challenges posed by new platforms. Farrukh holds a Ph.D. in Computer Science from Purdue University.

Research topics

• Computer Science
• Computer Security
• Data Mining
• Artificial Intelligence
• Machine Learning
• Human–computer interaction
• Medicine
• World Wide Web
• Data science
• Multimedia
• Computer vision

Selected publications

• Scoop: Mitigation of Recapture Attacks on Provenance-Based Media Authentication

  GetMobile Mobile Computing and Communications · 2026-04-23

  article

  Today, digital media is constantly produced and consumed in enormous volumes. We rely heavily on smartphone images and videos from daily social sharing and entertainment to critical tasks, such as verifying a new Uber driver's identity, online banking operations, or providing evidence in legal proceedings. However, continuous advances in digital media manipulation, especially with the introduction of generative AI, yield increasingly sophisticated deepfakes [14]. This poses a massive threat to society, facilitating the spread of fake news, misinformation, and personal slander that greatly endanger our perception of reality. Restoring trust in visual content has immense societal benefits, ensuring that organizations, institutions, and individuals can once again safely rely on the digital media they consume, restoring the principle of ''seeing is believing.'' A good solution must provide a reliable way to verify where, when, and how a piece of media was created, rather than relying solely on deepfake detection algorithms, which is unfortunately shaping up to be a never-ending arms race.

  PublisherDOI

• Follow My Eyes: Backdoor Attacks on VLM-based Scanpath Prediction

  arXiv (Cornell University) · 2026-04-09

  preprintOpen access

  Scanpath prediction models forecast the sequence and timing of human fixations during visual search, driving foveated rendering and attention-based interaction in mobile systems where their integrity is a first-class security concern. We present the first study of backdoor attacks against VLM-based scanpath prediction, evaluated on GazeFormer and COCO-Search18. We show that naive fixed-path attacks, while effective, create detectable clustering in the continuous output space. To overcome this, we design two variable-output attacks: an input-aware spatial attack that redirects predicted fixations toward an attacker-chosen target object, and a scanpath duration attack that inflates fixation durations to delay visual search completion. Both attacks condition their output on the input scene, producing diverse and plausible scanpaths that evade cluster-based detection. We evaluate across three trigger modalities (visual, textual, and multimodal), multiple poisoning ratios, and five post-training defenses, finding that no defense simultaneously suppresses the attacks and preserves clean performance across all configurations. We further demonstrate that backdoor behavior survives quantization and deployment on both flagship and legacy commodity smartphones, confirming practical threat viability for edge-deployed gaze-driven systems.

  PublisherDOI

• Follow My Eyes: Backdoor Attacks on VLM-based Scanpath Prediction

  ArXiv.org · 2026-04-09

  articleOpen access

  Scanpath prediction models forecast the sequence and timing of human fixations during visual search, driving foveated rendering and attention-based interaction in mobile systems where their integrity is a first-class security concern. We present the first study of backdoor attacks against VLM-based scanpath prediction, evaluated on GazeFormer and COCO-Search18. We show that naive fixed-path attacks, while effective, create detectable clustering in the continuous output space. To overcome this, we design two variable-output attacks: an input-aware spatial attack that redirects predicted fixations toward an attacker-chosen target object, and a scanpath duration attack that inflates fixation durations to delay visual search completion. Both attacks condition their output on the input scene, producing diverse and plausible scanpaths that evade cluster-based detection. We evaluate across three trigger modalities (visual, textual, and multimodal), multiple poisoning ratios, and five post-training defenses, finding that no defense simultaneously suppresses the attacks and preserves clean performance across all configurations. We further demonstrate that backdoor behavior survives quantization and deployment on both flagship and legacy commodity smartphones, confirming practical threat viability for edge-deployed gaze-driven systems.

  PublisherOA PDF

• Sentiment Analysis of Amazon Customer Reviews Using Machine Learning Models

  Pakistan Journal of Scientific Research · 2025-06-30

  articleOpen access1st authorCorresponding

  Sentiment analysis of Amazon customer reviews has become more important in today's digital marketplace, where understanding user mood directly impacts business strategies, product improvements, and customer satisfaction. Millions of reviews are created by everyday manual analysis, and there is an increasing demand for appropriate, automatic, and accurate ML solutions. This study addresses this need by implementing and comparing five ML models, which are D.T., which has 82.34% accuracy, Random Forest, which has 89.53% accuracy, Logistic regression, which has 91.52% accuracy, AdaBoost has 83.43% accuracy and XGboost, with 90.1% accuracy, to classify reviews as positive or negative. For the imbalanced dataset, the SMOTE technique was applied to balance sentiments. To address uneven distribution in mood analysis, SMOTE was used in this study. These discoveries provide businesses with actionable insights to automate review analysis, identify customer complaints, and make data-driven choices to boost products and services. This aims to classify user feedback into positive or negative categories. We trained our models on a dataset of 30,847 Amazon customer reviews covering various products and genres. This study shows the scalability of ML in actual-world mood categorization tasks and adds to the expanding body of work on applications. We also discuss the importance of striking a balance between computational effectiveness and model interpretability, particularly for parts that rely on illegal insights from massive amounts of unstructured feedback.

  PublisherOA PDFDOI

• Demo: UI Based Attacks in WebXR

  2025-06-23

  articleOpen access

  The WebXR API enables immersive AR/VR experiences directly through web browsers on head-mounted displays (HMDs). However, prior research shows that security-sensitive UI properties and the lack of an <iframe> like element that separates different origins can be exploited to manipulate user actions, particularly within the advertising ecosystem. In our prior work, we proposed five novel UI-based attacks in WebXR, targeting the ad ecosystem. This demo presents these attacks in a unified gaming application, embedding each into distinct interactive scenarios. Our work highlights the need to address design challenges and requirements for improving immersive web-based experiences. We provide our demo video at: https://youtu.be/lTBQbxnNq34.

  PublisherOA PDFDOI

• Blockchain-Based Fraud Detection: A Comparative Systematic Literature Review of Federated Learning and Machine Learning Approaches

  Electronics · 2025-12-17

  articleOpen access1st authorCorresponding

  This systematic literature review uses the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to assess progress in blockchain-based Federated learning (FL) and Machine Learning (ML) for detecting financial fraud over the last five years (2020–2025). An initial pool of 29,274 records identified across IEEE Xplore, ACM Digital Library, and ScienceDirect yielded 1585 peer-reviewed studies that met the inclusion criteria. Both qualitative and quantitative approaches were used. The examined papers were classified according to algorithm type, fraud types, and evaluation measures. Credit card fraud and cryptocurrency fraud dominated the literature, with supervised learning (e.g., XGBoost, 95% accuracy) and federated learning (e.g., FedAvg, 91% accuracy) emerging as dominant methodologies. Centralized ML outperforms FL in latency but poses privacy risks. FL–blockchain hybrids reduce false positives. While precision, recall, and F1-score are commonly used, few studies use cost-sensitive criteria. Future research should prioritize adaptive FL aggregation, privacy-preserving ML, and cross-industry collaboration.

  PublisherOA PDFDOI

• <i>Virtual</i> Reality, <i>Real</i> Problems: A Longitudinal Security Analysis of VR Firmware

  2025-11-19

  articleOpen access

  Virtual Reality (VR) technology is rapidly growing in recent years. VR devices such as Meta Quest 3 utilize numerous sensors to collect users' data to provide an immersive experience. Due to the extensive data collection and the immersive nature, the security of VR devices is paramount. Leading VR devices often adopt and customize Android systems, which makes them susceptible to both Android-based vulnerabilities and new issues introduced by VR-specific customizations (e.g., system services to support continuous head and hand tracking). While prior work has extensively examined the security properties of the Android software stack, how these security properties hold for VR systems remains unexplored. In this paper, we present the first comprehensive security analysis of VR firmware. We collect over 300 versions of VR firmware from two major vendors, Quest and Pico, and perform a longitudinal analysis across the kernel layer, the system binary and library layer, and the application layer. We have identified several security issues in these VR firmware, including missing kernel-level security features, insufficient binary hardening, inconsistent permission enforcement, and inadequate SELinux policy enforcement. Based on our findings, we synthesize recommendations for VR vendors to improve security and trust for VR devices. This paper will act as an important security resource for VR developers, users, and vendors, and will also direct future advancements in secure VR ecosystem

  PublisherDOI

• Poster: PeekXR: Understanding Privacy Leakages from Eye Gaze in Extended Reality

  2025-06-23

  articleOpen accessSenior author

  Extended Reality (XR) headsets are increasingly integrating eye tracking for enhanced user experience, adaptive interfaces, and foveated rendering. However, this rich biometric signal introduces new privacy risks. In this work, we demonstrate that eye-tracking data collected by commercial XR devices can be exploited to infer sensitive user activity. We leverage users' gaze sequences captured while interacting with a VR app to classify the type of content a user is watching. Our results reveal that eye movements alone, without any video or audio context, carry enough information to accurately predict content categories. We discuss the implications of this threat and outline how eye tracking can potentially be used to fingerprint applications and user behavior. This work is a step towards exposing and mitigating emerging privacy threats in immersive systems.

  PublisherOA PDFDOI

• Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms

  2025-05-12 · 13 citations

  articleOpen accessSenior author

  The widespread adoption of conversational AI platforms has introduced new security and privacy risks. While these risks and their mitigation strategies have been extensively researched from a technical perspective, users' perceptions of these platforms' security and privacy remain largely unexplored. In this paper, we conduct a large-scale analysis of over 2.5M user posts from the r/ChatGPT Reddit community to understand users' security and privacy concerns and attitudes toward conversational AI platforms. Our qualitative analysis reveals that users are concerned about each stage of the data lifecycle (i.e., collection, usage, and retention). They seek mitigations for security vulnerabilities, compliance with privacy regulations, and greater transparency and control in data handling. We also find that users exhibit varied behaviors and preferences when interacting with these platforms. Some users proactively safeguard their data and adjust privacy settings, while others prioritize convenience over privacy risks, dismissing privacy concerns in favor of benefits, or feel resigned to inevitable data sharing. Through qualitative content and regression analysis, we discover that users' concerns evolve over time with the evolving AI landscape and are influenced by technological developments and major events. Based on our findings, we provide recommendations for users, platforms, enterprises, and policymakers to enhance transparency, improve data controls, and increase user trust and adoption.

  PublisherOA PDFDOI

• Intermittent Power, Continuous Protection: Security and Privacy for Batteryless Devices in IoT

  2025-05-06

  articleOpen access

  Batteryless devices are gaining widespread adoption as they eliminate batteries through energy harvesting. This not only reduces electronic waste but also facilitates sensing and monitoring in physically inaccessible or challenging environments, such as implantable devices, smart agriculture, and forests. Thus, batteryless devices are increasingly being integrated into Internet of Things (IoT) environments, such as smart homes, healthcare, and agricultural systems, as well as cyber-physical systems (CPS), such as industrial plants and wearable technologies. In these contexts, batteryless devices often handle privacy-sensitive information and must interact seamlessly and securely with both other batteryless devices and traditional battery-powered devices to share data and perform tasks.

  PublisherOA PDFDOI

Frequent coauthors

• Z. Berkay Celik

  13 shared

• Muslum Ozgur Ozmen

  Purdue University System

  6 shared

• He Wang

  4 shared

• Muhammad bin Ibrahim

  Federal University Dutse

  3 shared

• Antonio Bianchi

  Purdue University System

  3 shared

• Güliz Seray Tuncay

  Google (United States)

  3 shared

• Doguhan Yeke

  Purdue University System

  3 shared

• Arjun Arunasalam

  2 shared

Education

• Ph.D., Computer Science

  Purdue University West Lafayette

  2023

Awards & honors

• ICS Students Win Global CPTC Cybersecurity Championship