Free AI Video Detector

How AI Video Detection Works

AI video detection is fundamentally more complex than image detection because it involves temporal analysis across multiple frames. FauxLens extracts key frames from uploaded videos and runs the full six-layer forensic analysis on each - GAN fingerprint detection, Error Level Analysis, PRNU noise fingerprinting, frequency domain analysis, metadata forensics, and neural classification. Our temporal consistency engine then analyzes frame-to-frame continuity across the entire sequence. AI video generators produce subtle flickering, warping, and physics violations between frames that are invisible to the human eye but detectable through mathematical analysis. We examine motion vectors to determine whether movement is organic or synthetically sampled, object persistence to check whether subjects maintain consistent geometry between frames, and the physical consistency of lighting and shadows throughout the clip. Audio-visual synchronization analysis catches cases where facial movements do not correspond to the audio track - the defining signal in lip-sync deepfakes. The result is a per-segment forensic report showing which frames raised flags and why, not just a single overall verdict.

AI Video Generators We Detect

FauxLens detects AI-generated video from all major generators currently in use. Sora by OpenAI uses a video diffusion transformer that leaves characteristic temporal coherence artifacts. Veo 2 by Google DeepMind, trained on YouTube-scale data, produces a distinct motion synthesis signature. Kling by Kuaishou has a different training data distribution that creates identifiable patterns in background elements and cloth simulation. Runway Gen-3 Alpha and Gen-3 Alpha Turbo produce per-frame GAN artifacts detectable in frequency space. Pika Labs, Luma Dream Machine, and Stable Video Diffusion each leave unique denoising artifacts across frame sequences. We also detect face-swap deepfakes created with DeepFaceLab, FaceSwap, Roop, and commercial real-time deepfake filter applications. Our detection models are updated continuously as new generators are released and existing ones are updated.

Temporal Inconsistency: Why Video Detection Differs From Photo Detection

A single AI-generated image carries generation artifacts in its pixels. A video adds an entirely new attack surface: the relationship between frames over time.

Current AI video generators struggle with object permanence - keeping every visual element consistent from one frame to the next. A person's shirt may shift color slightly between frames. A logo in the background may subtly change shape. Hair strands behave as if guided by different physics rules at different moments in the clip.

Physics consistency is another reliable signal. AI video generators simulate fluid dynamics, cloth movement, and hair response using learned approximations rather than physical models. Water splashes follow trajectories that are statistically plausible but not physically precise. Cloth folds in wind settle too quickly or too symmetrically. These deviations are measurable.

Motion blur is a third temporal signal. Real camera footage captures motion blur that is directionally consistent with the actual direction of movement. AI-generated motion blur is frequently rendered with incorrect directionality - blur that does not match the motion vector of the subject - a pattern detectable through optical flow analysis.

Eye blink timing in deepfake faces is measurably abnormal. Real people blink roughly 15 to 20 times per minute, with each blink lasting 150 to 400 milliseconds. Deepfake faces frequently skip blinks entirely or insert them at algorithmically distributed rather than neurologically natural intervals. FauxLens measures these temporal signals alongside per-frame forensics, which is why our video detection catches content that per-frame analysis alone would miss.

AI Video in Real-World Threats: 2025 to 2026

AI video has moved from research curiosity to active fraud tool. The most documented case is the 2024 Hong Kong CEO fraud incident, in which criminals used AI-generated video of senior executives in a live video call to convince a finance employee to transfer HK$200 million - equivalent to $25.6 million USD. The employee believed the faces and voices were real because the call appeared indistinguishable from a legitimate meeting. This was the first large-scale, documented financial fraud in which AI-generated video of specific individuals was used in real time to authorize a wire transfer.

In political disinformation, AI-generated video of politicians making statements they never made circulated on social media ahead of multiple national elections in 2024 and 2025. These clips were produced using face-swap technology and voice cloning, then distributed through coordinated networks before fact-checkers could respond. Several clips reached millions of views before removal.

Journalists working in conflict zones now encounter AI-generated evidence footage submitted by multiple parties - videos purporting to show atrocities, troop movements, or civilian casualties that cannot be verified through on-the-ground reporting alone. Veo 2-generated synthetic footage has been identified in documented disinformation operations in 2025.

Security teams at financial institutions now screen video calls for deepfake filters as standard fraud-prevention procedure. Legal professionals are beginning to submit AI video analysis reports as part of evidence authentication workflows. Journalists, lawyers, and security teams all need reliable video verification - which is exactly what FauxLens provides.

How to Get the Best Results From Video Analysis

The quality of your upload directly affects detection accuracy. Follow these guidelines for the most reliable results.

Trim to the suspicious section. FauxLens analyzes the full clip you upload. If you have a 10-minute interview and you suspect the first 2 minutes, upload those 2 minutes rather than the full recording. This reduces processing time and focuses the forensic analysis on the content that matters.

Upload at original quality whenever possible. Video that has been re-encoded, compressed for social media sharing, or converted between formats loses information that our forensic engine uses. The original file from a recording app or a direct platform download provides the strongest signal.

MP4 versus MOV: both formats are fully supported. MP4 is the standard for most device recordings. MOV files from Apple devices are accepted without conversion. Do not convert between formats before uploading - conversion introduces re-encoding that degrades forensic signals.

Bitrate matters more than frame rate for detection quality. A 30fps clip at a high bitrate is more analyzable than a 60fps clip that has been aggressively compressed. Prioritize bitrate over frame rate when you have a choice.

If your file exceeds the 100MB upload limit, trim the clip further using a lossless cutting tool that does not re-encode the video, such as FFmpeg with the -c copy flag. Re-encoding to reduce file size is acceptable as a last resort but will reduce some detection signal strength.