Researchers have built a tool that can identify the AI used to make a fake video
AI-generated videos have gotten so realistic that spotting a fake is difficult enough on its own. Figuring out which AI model actually created it is even harder. That’s exactly why researchers at UC Riverside built a tool that can do both.
- ▪AI-generated videos have gotten so realistic that spotting a fake is difficult enough on its own.
- ▪Figuring out which AI model actually created it is even harder.
- ▪That’s exactly why researchers at UC Riverside built a tool that can do both.
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| Original publisher | Digital Trends |
| Canonical URL | https://www.digitaltrends.com/computing/researchers-have-built-a-tool-that-can-identify-the-ai-used-to-make-a-fake-video/ |
| Publication time | Tue, 28 Jul 2026 17:40:22 +0000 |
| Retrieval time | 2026-07-28T17:50:05.610Z |
| Last seen | 2026-07-28T17:50:05.610Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | u0EFSpv3T1fa · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
AI-generated videos have gotten so realistic that spotting a fake is difficult enough on its own. Figuring out which AI model actually created it is even harder. That’s exactly why researchers at UC Riverside built a tool that can do both. It’s called SAGA, and it can trace a fake video back to the specific AI model that generated it. The project was led by UC Riverside doctoral researcher Rohit Kundu and electrical and computer engineering professor Amit K. Roy-Chowdhury, in collaboration with researchers from YouTube and Google DeepMind. How does SAGA figure out where a fake video came from? Bing Image Generator SAGA (Source Attribution of Generative AI Videos) looks for subtle visual patterns left behind unintentionally by AI video generators.
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