🚨AI-Generated Videos : Can We Really Tell If an AI-Generated Video Is Fake? 🎥

As use of major LLMs become more widespread, we have been seeing a crazy influx of AI-generated content on our feeds. It used to be fairly easy to spot - with a character sporting six fingers or something phasing through a solid object; but with recent improvements, these videos are getting harder and harder to tell.

This new paper, published mid-August 2026, is an interesting read, specifically surveying crisis-themed content, titled:

Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination.

They systematically evaluate how well detectors perform on crisis-themed content, how generation conditions affect detectability, how humans perceive authenticity, or whether detectors remain reliable after videos are shared and altered during social dissemination.

📦 Core Contribution: RA-Bench Dataset

The authors also introduce RA-Bench, a large-scale benchmark for AI-generated video detection that uses real videos as anchors, matching real-world crisis scenarios:

• Total videos: 17,886

• Real anchors: 1,830 authentic crisis/event videos across 10 social-risk categories

• Generated clips: 16,056 synthetic clips created from:

  • ✅ 4 open-source generators

  • ✅ 5 closed-source generators

• Designed to reflect real-world crisis contexts rather than generic footage

🔍 They then tested different types of AI detectors, including specialised deepfake detectors and multimodal AI models, like CNNSpot, UnivFD and ReStraV .

🚨The results were sobering.

1. Current detection methods were not reliable for crisis scenarios.

2. No single type of detector consistently worked across different AI generators. A detector might perform well on videos from one generator but struggle with another.

3. Impact of social media dissemination: Uploading, compressing, resizing, and reposting a video can destroy the subtle clues detectors rely on, making fake videos even harder to spot.

4. Videos that were most convincing to humans were also harder for AI detectors to identify.

This is especially concerning since crisis events are high-stakes environments where misinformation can escalate rapidly, highlighting the need for detectors robust to evolving video generators.