Chowdhury, FaridaAbedin, Jawaril MunshadMahin, Rifah TasnimAinun, Atika HossainIslam, Lamiya2025-09-152025-09-1520252025-06ID 24241199ID 24241190ID 24241188http://hdl.handle.net/10361/26722Cataloged from PDF version of thesis.Includes bibliographical references (pages 155-162).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Domestic violence remains a critical issue, especially in surveillance-heavy environments like Bangladesh where abusers often monitor their victims’ mobile activity. This research presents the iterative design and conceptual development of a discreet safety and support application for domestic abuse victims. Unlike traditional solutions, this mobile application is disguised as a benign utility app (e.g., a grocery list), ensuring discretion even under close monitoring. The app architecture follows a layered Four P’s Model: Preparation, Protection, Provision, and Prevention, aligning each feature with user safety goals. While designing, a user-centered approach was adopted, involving expert interviews, focus group discussions, feature assessment surveys, and victim testing across three design phases: hand-drawn paper prototypes, low-fidelity digital versions, and a fully navigable high-fidelity prototype. Each phase incorporated active feedback from survivors and professionals to ensure clarity, minimal cognitive load, and cultural relevance. Key functionalities include dummy interface switching, real/dummy login system, encrypted evidence logging, a Quick Exit button, and Bangla localization. Additionally, the app proposes two machine learning extensions: a voice-based distress and trigger word detection model using emotion recognition, and a conceptual risk prediction framework based on user-logged incidents. While not implemented due to time and development limitations, the models were architected using open-source datasets and preprocessing pipelines, ensuring future feasibility. By embedding iterative victim feedback and Human-Computer Interaction (HCI) principles throughout, this study demonstrates a survivor-informed, context-sensitive approach to mobile safety design. The final prototype serves as both a practical intervention model and a contribution to ongoing research in HCI, trauma-aware design, and machine learning for social good.176 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Domestic abuseSafety app designHuman-computer interactionMachine learningSurveillanceVoice emotion recognitionWomen’s safety technologyDisguised interfaceCognitive load minimizationHuman-computer interaction.Victims of family violence--Services for--Bangladesh.Machine learning.Automatic speech recognition.Electronic surveillance.Thesis