Authors - Vikas Pandey, Sanasam Chanu Inunganbi Abstract - Plagiarism has become a serious problem in universities, research organizations, and professional workplaces, affecting academic integrity and the originality of work. While cloud-based detection tools are widely used, they share a fundamental problem that rarely gets discussed openly: every document is required to be submitted and handed over to a third-party server. For unpublished research, legal drafts, or any sensitive material, this trade-off is not acceptable. The proposed method, named ZERO, takes a different approach and runs entirely on the local machine, watching the clipboard quietly in the background and scoring text against a local TF-IDF corpus in under 200 milliseconds, with no uploads, no accounts, and no data leaving the device. An optional web scanning module is available when broader source coverage is needed. On top of the similarity score, ZERO provides a word-level risk heatmap, a sentence-by-sentence originality breakdown, a stylometric module called Writing DNA, and a scan history timeline. Testing on 60 hand-labelled samples showed that a recalibrated scoring curve brings the average score on original technical writing down from 34.7% to 9.8%, while keeping verbatim-copy detection at 95%. API credentials are stored in the OS keychain, and inter-process communication is locked to a strict channel whitelist, making the application well-suited for confidential and pre-publication work.
Open Zoom