  Notwithstanding offering convenience and entertainment to society, Deepfake
face swapping has caused critical privacy issues with the rapid development of
deep generative models. Due to imperceptible artifacts in high-quality
synthetic images, passive detection models against face swapping in recent
years usually suffer performance damping regarding the generalizability issue.
Therefore, several studies have been attempted to proactively protect the
original images against malicious manipulations by inserting invisible signals
in advance. However, the existing proactive defense approaches demonstrate
unsatisfactory results with respect to visual quality, detection accuracy, and
source tracing ability. In this study, to fulfill the research gap, we propose
the first robust identity perceptual watermarking framework that concurrently
performs detection and source tracing against Deepfake face swapping
proactively. We assign identity semantics regarding the image contents to the
watermarks and devise an unpredictable and nonreversible chaotic encryption
system to ensure watermark confidentiality. The watermarks are encoded and
recovered by jointly training an encoder-decoder framework along with
adversarial image manipulations. Falsification and source tracing are
accomplished by justifying the consistency between the content-matched identity
perceptual watermark and the recovered robust watermark from the image.
Extensive experiments demonstrate state-of-the-art detection performance on
Deepfake face swapping under both cross-dataset and cross-manipulation
settings.
