Update README.md

README.md

@@ -813,4 +813,182 @@ configs:
   data_files:
   - split: train
     path: vit/train-*
+pretty_name: LoWRA-Bench
 ---
+
+# Dataset Card for the LoWRA Bench Dataset
+The ***Lo***RA ***W***eight ***R***ecovery ***A***ttack (LoWRA) Bench is a comprehensive 
+benchmark designed to evaluate Pre-Fine-Tuning (Pre-FT) weight recovery methods as presented
+in the "Recovering the Pre-Fine-Tuning Weights of Generative Models" paper.
+
+- [Task Details](#task-details)
+- [Dataset Description](#dataset-description)
+- [Dataset Structure](#dataset-structure)
+  - [Data Subsets](#data-subsets)
+  - [Data Fields](#data-fields)
+  - [Layer Merging Example](#layer-merging-example)
+- [Dataset Creation](#dataset-creation)
+- [Risks and Out-of-Scope Use](#risks-and-out-of-scope-use)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+
+- **🌐 Homepage:** 
+https://vision.huji.ac.il/spectral_detuning/
+- **🧑‍💻 Repository:**
+https://github.com/eliahuhorwitz/spectral_detuning
+- **📃 Paper:**
+http://arxiv.org/abs/
+- **✉️ Point of Contact:**
+[email protected] 
+
+
+## Task Details
+**Pre-Fine-Tuning Weight Recovery Attack Setting:** We uncover a vulnerability in LoRA fine-tuned models wherein an attacker is 
+able to undo the fine-tuning process and recover the weights of the original pre-trained model. 
+The setting for the vulnerability is as follows: 
+
+(a) The attacker only has access to n different LoRA fine-tuned models. 
+
+(b) The attacker assumes that all n models originated from the same source model.  
+
+(c) Using only the n visible models, the attacker attempts to recover the original source model.
+
+**Note: The attacker has no access to the low-rank decomposition of the fine-tuned models.**
+
+## Dataset Description
+
+The LoWRA Bench dataset is designed to evaluate the performance of Pre-FT weight recovery methods. 
+The dataset encompasses three pre-trained representative source models:
+1. A Vision Transformer (ViT) pre-trained on ImageNet-1K.
+2. Mistral-7B-v0.1.
+3. Stable Diffusion 1.5.
+
+These models collectively cover supervised and self-supervised objectives, spanning both vision and
+natural language processing (NLP) domains, as well as generative and discriminative tasks. 
+Notably, these models are widely used and deployed in numerous production systems.
+
+For each source model, we curate 15 LoRA models fine-tuned on diverse datasets, tasks, and objectives. 
+The dataset comprises a diverse array of layer types, including self-attention, cross-attention, 
+and MLPs. This diversity enables us to assess the generalization capabilities of Pre-FT methods. 
+The evaluation can be conducted on a per-model basis, per layer type, or layer depth, 
+allowing for a comprehensive analysis of Pre-FT methods. Overall, our dataset includes 544 source 
+model layers. When taking into account the fine-tuned LoRA layers, the dataset includes over 
+8,000 layers.
+
+
+## Dataset Structure
+The dataset contains 4 subsets, for each subset we curate 15 LoRA fine-tuned models. 
+Each row of the dataset represents a single layer that should be recovered and contains all the needed information for the recovery and numerical evaluation.
+In particular, for each layer, the dataset includes the original Pre-FT weights and the *unmerged* fine-tuned LoRA weight matrices. 
+We decided to provide the unmerged weights instead of the merged ones for two reasons:
+1. Providing the unmerged weights significantly reduces the storage size of the dataset (e.g., for a single Mistral subset this reduces the size from ~100GB to ~8GB).
+2. Providing the unmerged weights allows the dataset user to study the properties of the fine-tuned LoRA layers and may help when developing new methods.
+
+We leave the merging of the layers to the user, keep in mind this should be done carefully and tested to ensure the original Pre-FT weights are not simply 
+provided to the method verbatim. See [Layer Merging Example ](#layer-merging-example) for an example taken from our GitHub repository.
+
+
+### Data Subsets
+The table below describes the dataset subsets in detail:
+
+| Subset Name          | Pre-FT Model         | Task                          | Fine-tuning Task | # Pre-FT Layers | # Fine-tuned Layers |
+|----------------------|----------------------|-------------------------------|------------------|-----------------|---------------------|
+| vit                  | ViT                  | Image Classification          | VTAB-1K          | 24              | 360                 |
+| stable-diffusion-1.5 | Stable Diffusion 1.5 | Text-to-Image <br/>Generation | Personalization  | 264             | 3960                |
+| mistral-7b-v0.1-sft  | Mistral-7B-v0.1      | Text Generation               | UltraChat SFT    | 128             | 1920                |
+| mistral-7b-v0.1-dpo  | Mistral-7B-v0.1      | Text Generation               | UltraFeedback DPO| 128             | 1920                |
+
+
+### Data Fields
+As described above, each row of the dataset represents a single layer that should be recovered and contains the following fields: 
+
+    task_name - The name of the task the model was fine-tuned on (subset).
+    layer_model - In some cases a Pre-FT model has more than one model (e.g., Stable Diffusion fine-tuned both 
+                    the UNet and the Text Encoder). This field specifies the model the layer belongs to.
+    layer_name - The name of the layer in the Pre-FT model as it appears in the model state_dict.
+    pre_ft_name - The name of the Pre-FT model (e.g., runwayml/stable-diffusion-v1-5).
+    pre_ft_weight - The weight matrix of the Pre-FT models layer. 
+    lora_{lora_idx}_name - The name of the LoRA fine-tuned model.
+    lora_{lora_idx}_A_weight - The LoRA A weight matrix of the LoRA fine-tuned models layer.
+    lora_{lora_idx}_B_weight - The LoRA B weight matrix of the LoRA fine-tuned models layer.
+    lora_{lora_idx}_rank - The LoRA rank of the LoRA fine-tuned models layer.
+    lora_{lora_idx}_alpha - The LoRA alpha of the LoRA fine-tuned models layer.
+
+where `{lora_idx}` is the index of the LoRA fine-tuned model in the subset (there are 15 LoRA models per subset).
+
+
+### Layer Merging Example 
+The following code snippet demonstrates merging the LoRA fine-tuned weights with the Pre-FT weights. 
+```python
+def merge_lora_weights(args, layer_idx, device):
+    dataset = load_dataset(args.dataset, name=args.subset, cache_dir=args.cache_dir)
+    layer = deepcopy(dataset.with_format("torch")["train"][layer_idx])
+
+    merged_layer = {}
+
+    # Note: load the ground truth Pre-FT weights
+    merged_layer['layer_model'] = layer['layer_model']
+    merged_layer['layer_name'] = layer['layer_name']
+    merged_layer['pre_ft_name'] = layer['pre_ft_name']
+    W_pre_ft = deepcopy(layer['pre_ft_weight']).to(device).float()
+    merged_layer['pre_ft_weight'] = deepcopy(W_pre_ft)
+
+    # Note: merge the LoRA weights for all existing LoRA models
+    for lora_idx in args.lora_ids:
+        alpha = layer[f'lora_{lora_idx}_alpha']
+        rank = layer[f'lora_{lora_idx}_rank']
+        B = deepcopy(layer[f'lora_{lora_idx}_B_weight']).to(device).float()
+        A = deepcopy(layer[f'lora_{lora_idx}_A_weight']).to(device).float()
+
+        merged_layer[f'lora_{lora_idx}_name'] = layer[f'lora_{lora_idx}_name']
+        merged_layer[f'lora_{lora_idx}_rank'] = rank
+        merged_layer[f'lora_{lora_idx}_alpha'] = alpha
+        merged_layer[f'lora_{lora_idx}_merged_weights'] = W_pre_ft + ((alpha / rank * B) @ A)
+
+        assert torch.allclose(merged_layer['pre_ft_weight'], layer['pre_ft_weight'])
+        assert not torch.allclose(merged_layer[f'lora_{lora_idx}_merged_weights'], layer['pre_ft_weight'])
+        assert not torch.allclose(merged_layer[f'lora_{lora_idx}_merged_weights'], merged_layer['pre_ft_weight'])
+    return merged_layer
+```
+
+
+
+## Dataset Creation
+
+### Source Data
+- The fine-tuning of the ViT models was performed using the [PEFT](https://huggingface.co/docs/peft/en/index) library 
+  on various datasets from the [VTAB-1K](https://arxiv.org/abs/1910.04867) benchmark. 
+- The fine-tuned LoRA models for Stable Diffusion are taken from civitai and were fine-tuned by [RalFinger](https://civitai.com/user/RalFinger).  
+- The fine-tuning of Mistral was performed based on the Zephyr model as seen [here](https://github.com/huggingface/alignment-handbook/tree/main).
+
+For the full list of models and hyper-parameters see the appendix of the [paper](http://arxiv.org/abs/).
+
+
+## Risks and Out-of-Scope Use
+Our work uncovers a significant vulnerability in fine-tuned models, allowing attackers to 
+access pre-fine-tuning weights. While this discovery reveals potential security risks, 
+our primary objective is to advance the field of Machine Learning and raise awareness within the 
+research community about the existing vulnerabilities in current models.
+
+Instead of using the findings of this study to execute attacks, we advocate for their use by 
+model creators to enhance the safety and security of their models. By acknowledging and 
+addressing vulnerabilities, creators can proactively safeguard against potential threats.
+
+Following established practices in the cyber-security community, we emphasize the importance of open
+discussion and encourage the reporting of vulnerabilities. By fostering transparency and collaboration, 
+we can collectively create a safer environment for deploying machine learning models.
+
+## Considerations for Using the Data
+### Licensing Information
+[More Information Needed]
+
+### Citation Information
+If you use this dataset in your work please cite the following paper:
+
+**BibTeX:**
+
+[More Information Needed]
+
+