text
Spectral DeTuning is a new method that successfully recovers the original weights of generative models before they were fine-tuned with human feedback or other customization. It shows that pre-fine-tuning weights are recoverable and methods fine-tuned with LoRA can be susceptible to a new weight recovery type of attack.
Spectral DeTuning is a new method that successfully recovers the original weights of generative models before they were fine-tuned with human feedback or other customization. It shows that pre-fine-tuning weights are recoverable and methods fine-tuned with LoRA can be susceptible to a new weight recovery type of attack.
new_line
new_line
text
Key aspects of the paper:
Key aspects of the paper:
new_line
text
	•	It introduces Spectral DeTuning for reversing Low-rank Adaptation (LoRA) fine-tuning, targeting original weight restoration through spectral analysis.
	•	It introduces Spectral DeTuning for reversing Low-rank Adaptation (LoRA) fine-tuning, targeting original weight restoration through spectral analysis.
new_line
text
	•	LoWRA Bench Dataset: It introduces a dataset for testing Spectral DeTuning across various models and tasks, featuring extensive layers for comprehensive evaluation.
	•	LoWRA Bench Dataset: It introduces a dataset for testing Spectral DeTuning across various models and tasks, featuring extensive layers for comprehensive evaluation.
new_line
text
	•	It reveals LoRA fine-tuned models' vulnerability to weight recovery attacks, questioning the security of fine-tuning modifications.
	•	It reveals LoRA fine-tuned models' vulnerability to weight recovery attacks, questioning the security of fine-tuning modifications.
new_line
new_line
text
Congrats to the authors for their work!
Congrats to the authors for their work!
new_line
new_line
text
Paper: 
Paper: 
resource
https://huggingface.co/papers/2402.10208
paper
2402.10208
https://huggingface.co/papers/2402.10208
Recovering the Pre-Fine-Tuning Weights of Generative Models (2402.10208)
new_line
text
Dataset: 
Dataset: 
resource
https://huggingface.co/datasets/Eliahu/LoWRA-Bench
dataset
Eliahu/LoWRA-Bench
https://huggingface.co/datasets/Eliahu/LoWRA-Bench
new_line
text
Project page: 
Project page: 
link
https://vision.huji.ac.il/spectral_detuning/
https://vision.huji.ac.il/spectral_detuning/
new_line
text
Code: 
Code: 
link
https://github.com/eliahuhorwitz/Spectral-DeTuning
https://github.com/eliahuhorwitz/Spectral-DeTuning
Spectral DeTuning is a new method that successfully recovers the original weights of generative models before they were fine-tuned with human feedback or other customization. It shows that pre-fine-tuning weights are recoverable and methods fine-tuned with LoRA can be susceptible to a new weight recovery type of attack.

Key aspects of the paper:
	•	It introduces Spectral DeTuning for reversing Low-rank Adaptation (LoRA) fine-tuning, targeting original weight restoration through spectral analysis.
	•	LoWRA Bench Dataset: It introduces a dataset for testing Spectral DeTuning across various models and tasks, featuring extensive layers for comprehensive evaluation.
	•	It reveals LoRA fine-tuned models' vulnerability to weight recovery attacks, questioning the security of fine-tuning modifications.

Congrats to the authors for their work!

Paper: https://huggingface.co/papers/2402.10208
Dataset: https://huggingface.co/datasets/Eliahu/LoWRA-Bench
Project page: https://vision.huji.ac.il/spectral_detuning/
Code: https://github.com/eliahuhorwitz/Spectral-DeTuning
❤️
osanseviero PeepDaSlan9 samusenps clem deepkyu Svngoku sayhan ababio
👍
ioanacroi