Can liveness detection models generalise to synthetic media generation techniques they were never trained on? [D]

Reddit r/MachineLearning 05/21/26, 07:24 PM Papers

liveness-detection synthetic-media deepfake-detection generalization model-update security biometrics

Summary

This discussion examines whether liveness detection models trained on historical deepfake samples can generalize to new synthetic media generation techniques, questioning the update cycle for vendors claiming deepfake detection capabilities.

Most liveness detection systems in production today were built around a threat model where the attacker is submitting a static image or a basic replay video. The generation quality of current synthetic media is categorically different from what those training datasets captured. The question I keep coming back to is whether a model trained on historical deepfake samples can generalise to generation techniques that did not exist when the training data was assembled. And if the answer is no, what does the update cycle look like for vendors claiming deepfake detection as a core capability. I asked two identity verification vendors this directly and got answers that sounded confident without addressing the temporal gap between training data and current generation quality.

Original Article

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