Detection limitations
No forensic tool is perfect. Here is an honest account of what ClipForensics can and cannot do.
Honest Disclosure
Known limitations
We believe transparency about limitations is as important as detection accuracy. These are scenarios where ClipForensics may produce less reliable results.
Novel generation methods
New AI video generators (Sora, Kling, etc.) may produce artifacts that current modules are not trained to detect. We continuously update, but there is always a detection gap for the newest models.
Adversarial attacks
Sophisticated attackers can apply adversarial perturbations specifically designed to evade forensic analysis. No forensic tool is immune to targeted adversarial manipulation.
Heavy post-processing
Extreme compression, resolution downscaling, or heavy color grading can destroy forensic signals. Social media re-encoding (especially WhatsApp) significantly reduces detection accuracy.
Short clips
Videos under 3 seconds may not provide enough temporal data for motion, lip-sync, and consistency modules. Confidence intervals widen significantly for very short clips.
Static content
Slideshows, screen recordings of text, or videos with minimal motion give temporal and biological motion modules very little to work with.
Audio-only manipulation
If someone replaces only the audio track with a voice clone but leaves the video untouched, visual modules will not flag the video. Our audio modules help, but audio-only detection is a harder problem.
Context and intent
ClipForensics detects technical manipulation signals, not intent. A video can be technically authentic but misleading due to framing, editing, or context removal. We cannot assess journalistic or narrative manipulation.
Ground truth
Without access to the original source file, we cannot definitively prove authenticity. Our verdicts are probabilistic assessments, not absolute determinations.
Our Commitment
What we do about it
Continuous updates: We regularly update our detection modules as new generation methods emerge. Our research team monitors publications from major AI labs and adapts our analysis pipeline.
Confidence intervals: Every verdict includes a confidence score. When evidence is weak or conflicting, we produce an "Inconclusive" verdict rather than a false positive or negative.
Evidence transparency: Every report shows which modules contributed to the verdict and what specific evidence they found. Users can evaluate the reasoning themselves.
Responsible communication: We never claim 100% accuracy. Our marketing, documentation, and reports all reflect the probabilistic nature of forensic analysis.