Legibility vs. Extractability: Crafting Visual Defenses Against Automated OCR

Abstract

The rise of generative AI, particularly large language models (LLMs), has redefined problem-solving across domains—but it has also introduced new challenges in academic integrity. Students are increasingly using AI-powered Optical Character Recognition (OCR) tools to extract restricted content from screenshots, bypassing traditional safeguards that prevent copying and pasting. In this study, we investigate a set of visual defense techniques designed to counter automated OCR systems: (1) adversarial fonts crafted to disrupt character recognition, (2) color-based distortions that alter visual contrast, (3) animated interference lines that obstruct character boundaries, and (4) a novel cloud blur effect that dynamically follows the cursor to obscure localized text regions. We evaluate these strategies across multiple LLM-integrated OCR platforms—ChatGPT, DeepSeek, Claude, and Gemini. Our findings show that modern OCR tools remain largely unaffected by custom fonts, line obstructions, and color distortions. In contrast, the cloud blur technique significantly reduces OCR accuracy while preserving legibility for human readers. These results highlight dynamic, context-aware visual obfuscation as a promising and potentially future-proof solution for deterring AI-assisted text extraction. Cloud blur, in particular, emerges as the most effective approach, offering strong resistance to OCR while maintaining accessibility for legitimate human users. A live demo is available at https://cv.aut.ac.nz/nFonts.