AI video detection is the process of identifying synthetic or AI-generated video content created using artificial intelligence and machine learning techniques. This includes detecting videos generated by tools like OpenAI's SORA, Google Veo 3, deepfakes, face-swapped content, and other forms of synthetic media.

AI-generated videos we can detect include:

• SORA-generated videos - OpenAI's text-to-video AI model that creates realistic videos from text prompts
• Veo 3-generated videos - Google DeepMind's text-to-video AI model with synchronized audio
• Deepfakes - Face-swapped videos and manipulated content using deep learning
• Synthetic media - Entirely AI-generated scenes, people, and environments
• Face reenactment - Videos where facial expressions and movements are manipulated
• Audio-driven synthesis - Videos where AI makes people appear to say things they never said
• GAN-generated content - Videos created using Generative Adversarial Networks

As AI video generation tools like SORA and Veo 3 become more accessible, detecting AI-generated content has become crucial for verifying video authenticity.

Our AI video detection system uses advanced machine learning to analyze videos for signs of AI generation, including SORA-generated content, Veo 3 videos, deepfakes, and other synthetic media:

• Frame Analysis: We extract 16 frames from your video and analyze each one for AI-generated artifacts
• Deep Learning: An EfficientNet-B0 neural network trained on SORA, Veo 3, deepfake, and synthetic video datasets examines visual features
• Temporal Attention: We check for temporal inconsistencies typical of AI-generated videos, including SORA and Veo 3 content
• Pattern Recognition: The AI looks for generation artifacts from SORA, Veo 3, GANs, diffusion models, and deepfake techniques
• Confidence Scoring: You receive a probability score (0-100%) indicating likelihood of AI generation or manipulation

Our system is specifically trained to detect modern AI video generators like SORA and Veo 3, while also maintaining high accuracy on traditional deepfake detection.

Yes! Our AI video detection service is completely free to use for individual users. You can analyze videos for SORA-generated content, Veo 3 videos, deepfakes, and synthetic media without any cost, subscription, or registration required.

For enterprise users with high-volume needs or API access requirements, please contact us for pricing information.

We support all common video formats including:

• .mp4 (recommended)
• .mov
• .mkv
• .webm
• .avi

For best results, we recommend uploading videos in MP4 format with H.264 encoding.

Currently, you can upload videos up to 500 MB in size. For most cases, this allows for:

• Up to 5-10 minutes of HD video
• Up to 20-30 minutes of standard definition video

If you need to analyze larger files, consider compressing them first or contact us for enterprise solutions.

Our AI video detection model achieves 94%+ accuracy on benchmark datasets including FaceForensics++, Celeb-DF, DFDC, and synthetic video datasets containing SORA and Veo 3 style AI-generated content.

Accuracy varies by AI generation and manipulation type:

• SORA & Veo 3 Generated Videos: 95% accuracy on text-to-video AI content
• GAN-Generated Content: 97% accuracy on synthetic media
• Face Swap Deepfakes: 96% accuracy
• Face Reenactment: 93% accuracy
• Audio-Driven Synthesis: 91% accuracy
• Diffusion Model Videos: 94% accuracy

Important: No AI video detection system is 100% accurate. Results should be interpreted as probability scores, not absolute truth. As AI video generation technology like SORA and Veo 3 continues to evolve, we continuously update our models.

Yes, AI video detection systems can be challenged by:

• Very high-quality AI-generated videos from advanced models like SORA and Veo 3
• Novel AI video generation techniques not seen in training data
• Hybrid content mixing real footage with AI-generated elements
• Videos intentionally crafted to evade detection
• Post-processing that removes or hides AI generation artifacts
• Emerging AI video generators using new architectures

This is why we provide probability scores rather than binary "AI-generated/real" labels. We continuously update our models to stay ahead of new AI video generation techniques, including the latest versions of SORA, Veo 3, and other synthetic media tools.

An "Uncertain" result (typically 40-60% confidence) means our AI cannot confidently classify the video as AI-generated or authentic. This can happen when:

• The video quality is very poor or heavily compressed
• The AI-generated content (SORA, Veo 3, deepfake, etc.) is highly sophisticated
• The video contains a mix of real and AI-generated elements
• The content uses cutting-edge generation techniques we haven't fully trained on
• The video is ambiguous or borderline between real and synthetic

Recommendation: For uncertain results when detecting SORA, Veo 3, or other AI-generated videos, we suggest:

• Manually reviewing the video using our comprehensive guide
• Checking the source, metadata, and context of the video
• Looking for telltale signs of AI generation (unnatural motion, temporal inconsistencies, artifacts)
• Consulting forensic experts for critical decisions

No. Our service is provided for informational purposes only and should not be used as sole evidence in legal proceedings.

For legal matters, you should:

• Consult with certified forensic experts
• Use multiple independent verification methods
• Obtain court-admissible digital forensic analysis
• Follow proper chain-of-custody procedures

Our results can serve as an initial screening tool but require professional verification for legal use.

No! We take your privacy seriously:

• ✅ Videos are automatically deleted immediately after analysis
• ✅ We do NOT store video files on our servers
• ✅ URLs you submit are not logged or saved
• ✅ Only anonymous analysis results are temporarily cached (no video content)

Your privacy is our top priority. Videos exist on our servers only for the brief time needed to process them (typically under 30 seconds).

Yes. We use industry-standard encryption:

• HTTPS/TLS encryption for all data transmission
• Encrypted uploads to our cloud storage (Wasabi)
• Secure processing in isolated environments

Your videos are protected during upload, processing, and deletion.

Analysis results are cached anonymously (identified only by video hash, not personal information) and expire automatically after a period of time. Since we don't store your video files and results aren't linked to your identity, there's no personal data to delete.

If you have specific privacy concerns, please contact us.

No. We do not sell, share, or provide your videos or analysis results to any third parties. Period.

The only exception would be if required by law enforcement with proper legal authority.

We use a custom-trained EfficientNet-B0 architecture enhanced with temporal attention mechanisms.

Key features:

• Trained on 500,000+ videos from diverse datasets
• Analyzes 16 uniformly sampled frames per video
• Examines facial regions, edges, compression artifacts, and temporal consistency
• Optimized for both accuracy and speed

Learn more on our About Technology page.

Analysis typically takes under 30 seconds for most videos, depending on:

• Video length and resolution
• Current server load
• Your internet connection speed (for uploads)

Short clips may complete in as little as 10-15 seconds.

We're currently developing an API for enterprise customers with high-volume needs.

If you're interested in API access for your business, please contact us with your use case and volume requirements.

Our current model is optimized for video analysis because it uses temporal consistency across frames as a key detection signal.

For static images, the analysis would be less accurate. We recommend using specialized image deepfake detectors for still photos.

Image detection is on our roadmap for future development.

We continuously monitor deepfake technology developments and update our model regularly to maintain effectiveness against new techniques.

Major updates typically occur:

• When new deepfake generation methods emerge
• When we acquire significant new training data
• Based on user feedback and failure cases

Our commitment is to stay ahead of the evolving deepfake landscape.