END^2: Robust Dual-Decoder Watermarking Framework Against Non-Differentiable Distortions
Authors: Nan Sun, Han Fang, Yuxing Lu, Chengxin Zhao, Hefei Ling
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our scheme outperforms state-of-the-art algorithms under various non-differentiable distortions. Our primary interest is to explore the performance of END2 under fully non-differentiable distortion. |
| Researcher Affiliation | Academia | Nan Sun1, Han Fang2, Yuxing Lu3, Chengxin Zhao1, Hefei Ling1* 1 Huazhong University of Science and Technology 2 National University of Singapore 3 Peking University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: END2 Pipeline Require: Image-Message pairs (x, m) from dataset, Encoder fθ, Teacher Decoder gt and Parameters δt, Student Decoder gs and Parameters δs, Distortion Function ψ, Momentum Coefficient τ, Optimizer op, Swapping interval k Ensure: Optimized Encoder and Decoders 1: for i, (x, m) in enumerate(dataset) do 2: ˆx fθ(x, m) # Encode image with watermark 3: x sg[ψ(ˆx)] # Apply distortion attack 4: 5: # extract message and feature from encoded images 6: ˆmt, zt gt(ˆx) # Teacher decoder 7: ˆms, zs gs( x) # Student decoder 8: 9: L Ls,t + Lmsg + Lquality # Calculate total loss 10: L.backward() # Backpropagate 11: op.step() # Update model parameters 12: 13: # Momentum updating strategy 14: δt τδt + (1 τ)δs 15: # Swapping Learning strategy 16: if (i + 1) mod k = 0 then 17: gt, gs gs, gt 18: end if 19: end for |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | The model is trained on DIV2K(Agustsson and Timofte 2017). Specifically, we randomly select a block of size 128 128 from the training set as the cover image, and randomly sample a bit stream of length 30 to use as watermarking information for embedding. To evaluate the model s generalization, we also randomly select 2000 images from the COCO (Lin et al. 2014) and Image Net (Deng et al. 2009) datasets for testing. |
| Dataset Splits | Yes | Specifically, we randomly select a block of size 128 128 from the training set as the cover image, and randomly sample a bit stream of length 30 to use as watermarking information for embedding. To evaluate the model s generalization, we also randomly select 2000 images from the COCO (Lin et al. 2014) and Image Net (Deng et al. 2009) datasets for testing. |
| Hardware Specification | Yes | The batch size is set to 32, and the model is trained for a total of 5000 epochs on a NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using 'PIL package in Python' and 'Adam optimizer' but does not specify exact version numbers for these or other key software components, which is necessary for reproducibility. |
| Experiment Setup | Yes | We utilize the Adam optimizer with a fixed learning rate of 8e 4 for training. The batch size is set to 32, and the model is trained for a total of 5000 epochs on a NVIDIA RTX 3090 GPU. ... where λ1, λ2, λ3 are three hyperparameters used to balance different losses, defaulting to 0.01, 8, 5. ... where τ is the weight factor and η is the learning rate, defaulting to 0.999 and 8e 4. ... swapping the two decoders after each k training batches, with k defaulting to 1. |