SEAL: Entangled White-box Watermarks on Low-Rank Adaptation
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.
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Giyeong Oh
claimed_verified
2025-01-20T09:28:57.015Z
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/avatars/99a3aeadcc81ef85164cdfb6ab186b17.svg
Giyeong Oh
user
BootsofLagrangian
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Saejin Kim
claimed_verified
2025-01-20T13:16:39.750Z
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SJKIM
user
Steamout
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Woohyun Cho
678dfb39f002f862857e90c2
Sangkyu Lee
678dfb39f002f862857e90c3
Jiwan Chung
admin_assigned
2025-01-21T14:22:48.531Z
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Jiwan Chung
user
jiwan-chung
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Dokyung Song
admin_assigned
2025-01-21T14:22:42.282Z
65ab767ca92a64ef5b9c8423
/avatars/00abf7a05fd86a08587f72cab5b3cff3.svg
Dokyung Song
user
dokyungs
678dfb39f002f862857e90c5
Youngjae Yu
extracted_pending
2025-01-20T07:28:58.615Z
6504777fb1da3747a05160c4
/avatars/b777d98a5ff971ddb4c3e1060bb3e070.svg
Youngjae Yu
user
yjyu
SEAL: Entangled White-box Watermarks on Low-Rank Adaptation
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.
SEAL is a watermarking technique for LoRA models that embeds a non-trainable matrix to protect ownership without affecting performance or security.
LoRA SEAL whitebox watermarking non-trainable matrix entanglement commonsense reasoning textual/visual instruction tuning text-to-image synthesis removal attacks obfuscation attacks ambiguity attacks