Nov 21, 2025

Mirage

Noshaba Cheema, Amir Razagh, Vlada Kandyba

The deepfake detection market is rapidly growing in response to the explosion of AI-generated fake content. In 2023 the global deepfake detection market was valued at only about $213 million, but it is forecast to surge to roughly $3.46 billion by 2031 – a 40%+ CAGR growth trajectory. This explosive growth is driven by the rising threat of deepfake fraud and misinformation across industries. For example, identity fraud attacks using deepfakes jumped 31× from 2022 to 2023 in one report, underscoring the urgent need for reliable detection. Major sectors are already seeking solutions: banking and financial services want to prevent deepfake identity/document fraud; media and entertainment companies need tools to preserve trust and authenticity by stopping the spread of fake videos and images; governments and defense agencies view deepfakes as a national security risk; and social media platforms are exploring anti-deepfake tech to maintain a safe environment for users. Journalists and fact-checkers in particular are looking for ways to verify images, videos, or audio clips, as public trust in media is eroded by convincing fake visuals. This is where Mirage fits in: it targets a clear pain-point across these domains by offering a multi-modal deepfake detection agent (covering text, images, and video) that delivers a “genuineness score” for content. Its use of blockchain (NEAR Protocol) for immutable logging of verification results is a unique differentiator, adding transparency and public trust to the detection (similar in spirit to initiatives like Project Origin which use tamper-proof ledgers to certify media authenticity). Likewise, leveraging a TEE (Trusted Execution Environment) for the AI agent addresses security and privacy concerns, ensuring sensitive analyses are done in a private, verifiable manner. Overall, Mirage is well-positioned to serve its target audiences – enterprises, social media platforms, and journalists – by filling the market need for a trustworthy, real-time deepfake detection solution that is both highly accurate and credibly transparent (thanks to on-chain verification).

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Cite this work

@misc {

title={

(HckPrj) Mirage

},

author={

Noshaba Cheema, Amir Razagh, Vlada Kandyba

},

date={

11/21/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

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This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
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