ModalFidelity: Routing Modalities for Deepfake Detection on a Budget
Quick summary
arXiv:2609.38246v1 Announce Type: cross Abstract: Deepfakes no longer need to fake a whole video. Generators that read the transcript now alter only the few seconds in which a video's meaning turns, so a forgery hides in a small, unknown fraction of the video. Yet detectors still read every one-second window of both the audio and image streams, spending nearly all of their compute where nothing was altered. We observe that deciding where to look is far cheaper than looking. We present ModalFidelity, a lightweight router that previews each window and decides, before any forensic detector runs,
Key takeaways
- arXiv:2609.38246v1 Announce Type: cross Abstract: Deepfakes no longer need to fake a whole video.
- Generators that read the transcript now alter only the few seconds in which a video's meaning turns, so a forgery hides in a small, unknown fraction of the video.
- Yet detectors still read every one-second window of both the audio and image streams, spending nearly all of their compute where nothing was altered.
Why it matters
“ModalFidelity: Routing Modalities for Deepfake Detection on a Budget” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.
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