"Gazan" journalist posts fake video of IDF soldiers burning U.S. Flag, Internet erupts | WATCH
AI-Generated Video Falsely Depicts Israeli Soldiers Burning US Flag, Sparks Online Misinformation Wave

In the midst of escalating geopolitical tensions, a viral video purporting to show Israeli soldiers burning an American flag has been debunked as an AI-generated fabrication, according to multiple fact-checking sources and digital forensics experts.
The footage, which depicts a group of uniformed individuals dousing a US flag with liquid before setting it ablaze, began circulating widely on social media platforms like X (formerly Twitter) and TikTok earlier this week. Accompanying captions falsely claimed the act was carried out by members of the Israel Defense Forces (IDF), fueling accusations of anti-American sentiment amid ongoing conflicts in the Middle East.
However, independent analyses reveal the video is not authentic. Created using OpenAI's Sora AI tool, the clip exhibits classic hallmarks of synthetic media: distorted facial features, unnatural hand movements, and inconsistencies in physics, such as the flag igniting prematurely before accelerant is fully applied. Watermarks from Sora were partially obscured in shared versions through cropping and overlays, but remnants remain visible in higher-resolution copies.
Tracing its origins, the video first appeared on a TikTok account known for producing AI content (@naksu_ah), where it was initially labeled as generated imagery. Once reposted without context, it was weaponized by propaganda accounts to exacerbate divisions, aligning with a surge in AI-fueled disinformation targeting international relations.
Fact-checkers, including organizations like D-Intent Data, have labeled the claims "completely fake," urging users to verify sources before sharing.
Social media companies have yet to comment on removal efforts, but users are advised to look for AI indicators and cross-reference with reputable outlets. As digital tools evolve, so too does the need for vigilance against manipulated content.