TraceEvader: Making DeepFakes More Untraceable via Evading the Forgery Model Attribution
Mengjie Wu,
Jingui Ma,
Run Wang
et al.
Abstract:In recent few years, DeepFakes are posing serve threats and concerns to both individuals and celebrities, as realistic DeepFakes facilitate the spread of disinformation. Model attribution techniques aim at attributing the adopted forgery models of DeepFakes for provenance purposes and providing explainable results to DeepFake forensics. However, the existing model attribution techniques rely on the trace left in the DeepFake creation, which can become futile if such traces were disrupted. Motivated by our obse… Show more
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