CensorX: AI powered real-time content monitoring and safety app
| dc.contributor.author | Desai, Geeta [Guide] | |
| dc.contributor.author | Shah, Aqsa [22EC03] | |
| dc.contributor.author | Kaldane, Faizan [22EC20] | |
| dc.contributor.author | Shaikh, Maheen [23DEC04] | |
| dc.date.accessioned | 2026-09-01T10:46:10Z | |
| dc.date.available | 2026-09-01T10:46:10Z | |
| dc.date.issued | 2026-06 | |
| dc.description.abstract | The rapid growth of digital communication platforms has significantly increased users’ exposure to online harassment, hate speech, and explicit content, particularly within private and encrypted messaging environments. While these platforms provide enhanced privacy through end-to-end encryption, they also limit the effectiveness of traditional moderation systems. Existing solutions primarily rely on server-side or cloud-based content analysis, which introduces concerns related to user privacy, higher latency, and limited applicability in encrypted communication systems.To address these challenges, this paper proposes CensorX, an on-device, real-time harassment detection and content censoring framework designed specifically for Android-based systems. Unlike conventional approaches, the proposed system operates entirely on the client device, ensuring that sensitive user data is not transmitted to external servers. CensorX leverages lightweight Natural Language Processing (NLP) models combined with rule-based filtering techniques to analyze textual content in real time. The system utilizes Android system-level services to monitor user input across applications without requiring modifications to third-party platforms. The core methodology involves a hybrid detection mechanism where a pre-trained NLP classification model identifies harmful or abusive patterns based on contextual understanding, while a rule-based layer enhances detection accuracy by capturing explicit keywords and predefined patterns. This combination enables efficient processing with low computational overhead, making it suitable for continuous background operation on mobile devices. Experimental evaluation of the proposed system demonstrates promising performance. CensorX achieves a detection accuracy of 92.4%, with a precision of 90.1% and recall of 93.6% in identifying harassment-related content. The system maintains an average processing latency of less than 120 milliseconds per input, ensuring realtime responsiveness. Additionally, CPU usage remains below 8% on average, indicating minimal performance impact on the device. These results highlight the effectiveness and feasibility of deploying on-device AI-based moderation systems for enhancing digital safety while preserving user privacy. Keywords: Harassment Detection, Content Censoring, On Device AI, Natural Language Processing, Mobile Security, Digital Safety, Android Accessibility . | |
| dc.identifier.uri | http://aiktc.ndl.gov.in/handle/123456789/4614 | |
| dc.language.iso | en_US | |
| dc.publisher | AIKTC | |
| dc.title | CensorX: AI powered real-time content monitoring and safety app | |
| dc.type | Project Report |