SEAL: Entangled White-box Watermarks on Low-Rank Adaptation
Authors: name: Giyeong Oh, name: Saejin Kim, name: Woohyun Cho, name: Sangkyu Lee, name: Jiwan Chung, name: Dokyung Song, name: Youngjae Yu

Recently, LoRA and its variants have become the de facto strategy for
training and sharing task-specific versions of large pretrained models, thanks
to their efficiency and simplicity. However, the issue of copyright protection
for LoRA weights, especially through watermark-based techniques, remains
underexplored. To address this gap, we propose SEAL (SEcure wAtermarking on
LoRA weights), the universal whitebox watermarking for LoRA. SEAL embeds a
secret, non-trainable matrix between trainable LoRA weights, serving as a
passport to claim ownership. SEAL then entangles the passport with the LoRA
weights through training, without extra loss for entanglement, and distributes
the finetuned weights after hiding the passport. When applying SEAL, we
observed no performance degradation across commonsense reasoning,
textual/visual instruction tuning, and text-to-image synthesis tasks. We
demonstrate that SEAL is robust against a variety of known attacks: removal,
obfuscation, and ambiguity attacks.