Antidistillation Fingerprinting (ADFP)
An output fingerprinting method designed to preserve detectable statistical signatures after distillation
#ADFP#Antidistillation Fingerprinting#fingerprinting#distillation defense#model protection
What is Antidistillation Fingerprinting (ADFP)?
ADFP is an output fingerprinting approach that inserts statistical signatures in ways designed to survive distillation better than conventional watermarking.
Its core idea is to concentrate signatures on token positions that are more likely to transfer to student models, improving post-distillation detectability.
Why does it matter?
Conventional output watermarking can be weakened or removed through paraphrasing, post-processing, or distillation.
ADFP is being studied as a way to address that weakness, making it a notable candidate technology for AI model IP protection.
Practical checkpoints
- Research-stage reality: Promising, but large-scale commercial deployment remains limited.
- Layered defense needed: Use with anomaly detection, access control, and legal enforcement processes.
- Evidence readiness: Combine with logs, model versioning, and policy records to strengthen traceability and legal usability.
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