Yu Wu, Qian Wang, Huayang Huang
We lifted 16 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Hannah1102/ROBIN | canonical | 5 of 16 |
| Function | Status | Where it lives |
|---|---|---|
| approx_standard_normal_cdf | Ran | Hannah1102/ROBIN/guided_diffusion/losses.py pointer only (licence: NONE) · get_code("cfd76fd0d89574a4") |
| convert_to_custom_text_state_dict | Ran | Hannah1102/ROBIN/open_clip/model.py pointer only (licence: NONE) · get_code("f19962ebb134b3d7") |
| discretized_gaussian_log_likelihood | Ran | Hannah1102/ROBIN/guided_diffusion/losses.py pointer only (licence: NONE) · get_code("cd33283d615fb3d7") |
| get_cast_dtype | Ran | Hannah1102/ROBIN/open_clip/model.py pointer only (licence: NONE) · get_code("dcd422d66b0581d8") |
| normal_kl | Ran | Hannah1102/ROBIN/guided_diffusion/losses.py pointer only (licence: NONE) · get_code("cf2798b666b231ca") |
| backward_ddim | Not yet run | Hannah1102/ROBIN/inverse_stable_diffusion.py pointer only (licence: NONE) · get_code("b9d521d7021e46bb") |
| build_model_from_openai_state_dict | Not yet run | Hannah1102/ROBIN/open_clip/model.py pointer only (licence: NONE) · get_code("57f1602ed20a9c21") |
| create_dataloader | Not yet run | Hannah1102/ROBIN/gen_watermark.py pointer only (licence: NONE) · get_code("c00a1b3a3bfccabb") |
| forward_ddim | Not yet run | Hannah1102/ROBIN/inverse_stable_diffusion.py pointer only (licence: NONE) · get_code("3b89ab9f937dccdd") |
| gather_features | Not yet run | Hannah1102/ROBIN/open_clip/loss.py pointer only (licence: NONE) · get_code("ddcbd45e940484ee") |
| get_pred_ori | Not yet run | Hannah1102/ROBIN/stable_diffusion_robin.py pointer only (licence: NONE) · get_code("8c930cf169a119b1") |
| hf_read_jsonlines | Not yet run | Hannah1102/ROBIN/io_utils.py pointer only (licence: NONE) · get_code("075fbc6126eeefe6") |
| load_jsonlines | Not yet run | Hannah1102/ROBIN/io_utils.py pointer only (licence: NONE) · get_code("81cef8be4a138b63") |
| prepare_inputs_for_generation | Not yet run | Hannah1102/ROBIN/open_clip/coca_model.py pointer only (licence: NONE) · get_code("fb651d0a97fd4d3f") |
| register_pooler | Not yet run | Hannah1102/ROBIN/open_clip/hf_model.py pointer only (licence: NONE) · get_code("2a377da4a76a2d44") |
| resolve_globs | Not yet run | Hannah1102/ROBIN/io_utils.py pointer only (licence: NONE) · get_code("afbb28c04b32db08") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Watermarking generative content serves as a vital tool for authentication, ownership protection, and mitigation of potential misuse. Existing watermarking methods face the challenge of balancing robustness and concealment. They empirically inject a watermark that is both invisible and robust and passively achieve concealment by limiting the strength of the watermark, thus reducing the robustness. In this paper, we propose to explicitly introduce a watermark hiding process to actively achieve concealment, thus allowing the embedding of stronger watermarks. To be specific, we implant a robust watermark in an intermediate diffusion state and then guide the model to hide the watermark in the final generated image. We employ an adversarial optimization algorithm to produce the optimal hiding prompt guiding signal for each watermark. The prompt embedding is optimized to minimize artifacts in the generated image, while the watermark is optimized to achieve maximum strength. The watermark can be verified by reversing the generation process. Experiments on various diffusion models demonstrate the watermark remains verifiable even under significant image tampering and shows superior invisibility compared to other state-of-the-art robust watermarking methods. Code is available at https://github.com/Hannah1102/ROBIN.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2411.03862")
get_code_for_paper("2411.03862")
have("2411.03862")
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