  As deep generative models advance, we anticipate deepfakes achieving
"perfection"-generating no discernible artifacts or noise. However, current
deepfake detectors, intentionally or inadvertently, rely on such artifacts for
detection, as they are exclusive to deepfakes and absent in genuine examples.
To bridge this gap, we introduce the Rebalanced Deepfake Detection Protocol
(RDDP) to stress-test detectors under balanced scenarios where genuine and
forged examples bear similar artifacts. We offer two RDDP variants:
RDDP-WHITEHAT uses white-hat deepfake algorithms to create 'self-deepfakes,'
genuine portrait videos with the resemblance of the underlying identity, yet
carry similar artifacts to deepfake videos; RDDP-SURROGATE employs surrogate
functions (e.g., Gaussian noise) to process both genuine and forged examples,
introducing equivalent noise, thereby sidestepping the need of deepfake
algorithms.
  Towards detecting perfect deepfake videos that aligns with genuine ones, we
present ID-Miner, a detector that identifies the puppeteer behind the disguise
by focusing on motion over artifacts or appearances. As an identity-based
detector, it authenticates videos by comparing them with reference footage.
Equipped with the artifact-agnostic loss at frame-level and the
identity-anchored loss at video-level, ID-Miner effectively singles out
identity signals amidst distracting variations. Extensive experiments comparing
ID-Miner with 12 baseline detectors under both conventional and RDDP
evaluations with two deepfake datasets, along with additional qualitative
studies, affirm the superiority of our method and the necessity for detectors
designed to counter perfect deepfakes.
