title: SEAL: Entangled White-box Watermarks on Low-Rank Adaptation
summary: 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.
paper_authors._id: 678dfb39f002f862857e90bf
paper_authors.name: Giyeong Oh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-20T09:28:57.015Z
paper_authors._id: 63d93667255ef6add20f9272
paper_authors.avatarUrl: /avatars/99a3aeadcc81ef85164cdfb6ab186b17.svg
paper_authors.fullname: Giyeong Oh
paper_authors.type: user
paper_authors.user: BootsofLagrangian
paper_authors._id: 678dfb39f002f862857e90c0
paper_authors.name: Saejin Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-20T13:16:39.750Z
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paper_authors.fullname: SJKIM
paper_authors.type: user
paper_authors.user: Steamout
paper_authors._id: 678dfb39f002f862857e90c1
paper_authors.name: Woohyun Cho
paper_authors._id: 678dfb39f002f862857e90c2
paper_authors.name: Sangkyu Lee
paper_authors._id: 678dfb39f002f862857e90c3
paper_authors.name: Jiwan Chung
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-21T14:22:48.531Z
paper_authors._id: 60d74d1affe0328e0167dc5f
paper_authors.avatarUrl: /avatars/9b1a2df9402e9c26e1eb7c818af9bae0.svg
paper_authors.fullname: Jiwan Chung
paper_authors.type: user
paper_authors.user: jiwan-chung
paper_authors._id: 678dfb39f002f862857e90c4
paper_authors.name: Dokyung Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-21T14:22:42.282Z
paper_authors._id: 65ab767ca92a64ef5b9c8423
paper_authors.avatarUrl: /avatars/00abf7a05fd86a08587f72cab5b3cff3.svg
paper_authors.fullname: Dokyung Song
paper_authors.type: user
paper_authors.user: dokyungs
paper_authors._id: 678dfb39f002f862857e90c5
paper_authors.name: Youngjae Yu
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-01-20T07:28:58.615Z
paper_authors._id: 6504777fb1da3747a05160c4
paper_authors.avatarUrl: /avatars/b777d98a5ff971ddb4c3e1060bb3e070.svg
paper_authors.fullname: Youngjae Yu
paper_authors.type: user
paper_authors.user: yjyu
paper_title: SEAL: Entangled White-box Watermarks on Low-Rank Adaptation
paper_summary: 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.
paper_ai_summary: SEAL is a watermarking technique for LoRA models that embeds a non-trainable matrix to protect ownership without affecting performance or security.
paper_ai_keywords: LoRA SEAL whitebox watermarking non-trainable matrix entanglement commonsense reasoning textual/visual instruction tuning text-to-image synthesis removal attacks obfuscation attacks ambiguity attacks