Evading Online Keyword Censorship (PixelStacks)
Sensitive information, such as banned books and protest organization communications, require secure channels to evade private or state surveillance. This project aims to provide public access to a secure steganographic system for additional privacy in communications and underground publishing.
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A narrated presentation about the research topic submitted to the University of Washington Undergraduate Research Symposium.
Abstract
Internet censorship is often enforced via automated keyword filtering with a keyword list, implemented on a platform or state level. Individual users who discuss sensitive issues online may find their social media posts are made invisible or removed and may even have their accounts shut down.
In this literature, we propound steganography as a publishing infrastructure against internet keyword censorship. We conducted an experiment to confirm our hypothesis that steganography is a viable way to transmit sensitive information over censored networks. The end product is a publicly available, online steganography tool equipped with an improved version of an existing steganographic algorithm that uses random pixel selection to minimize noise in the stego image. We evaluated the proposed method against its predecessors and found that our method gives a higher quality of stego images in terms of peak signal-to-noise ratio and other common metrics used to measure image steganographic systems.