隨著科技的進步與資訊科學的發展，大量的資訊處理自動化逐漸取代傳統人工技術，然而不恰當地使用自動化技術，卻可能危害人類的權益與空間。為避免過度濫用機器自動化對人類所造成的災害，本研究根據不同的適用情境，分別提出以靜態及動態圖型為基礎的人機區分方法，透過簡單的影像處理技術，產生機器難以分析但人類能夠易於判別的人機辨識影像。並且由認知的角度，設計實驗進一步探討人類視覺優勢以及接受度，作為影像產生時的標準。最後，提出人機區分技術與應用情境整合實作的方法，以觀實效。 / The idea of using a computer program to distinguish humans from machines, sometimes referred to as the “Reverse Turing Test”, has emerged only quite recently. The term CAPTCHA, which stands for “Completely Automated Public Turing Test to Tell Computers and Humans Apart", is defined as: “a program that can generate and grade tests that: □ Most human can pass but □ Current computer program can’t pass! “ In this thesis, a texture-image based approach is developed to encode text information in such a way that machine vision algorithms will experience significant difficulties while human can extract the embedded text effortlessly. Both static images and dynamic sequences will be explored. It is anticipated that the cost of storing, and subsequently decoding information from such visual patterns will be prohibitedly high, both in terms of time and space complexity. To validate the postulation, fundamental principles of the human cognitive process will be examined. Experiments will also be carried out to gather user feedback and investigate the limitations of human visual systems. Finally, several application scenarios that call for the integration of a CAPTCHA will be identified and discussed.