ForgDiffuser: General Image Forgery Localization with Diffusion Models
Authors: Mengxi Wang, Shaozhang Niu, Jiwei Zhang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on six benchmark datasets demonstrate that Forg Diffuser outperforms existing mainstream GIFL methods in both localization accuracy and robustness, especially under challenging manipulation conditions. In this subsection, we conduct ablation experiments on the proposed Forg Diffuser to verify the effectiveness of each designed module. Quantitative comparisons: Table 1 demonstrates the quantitative results of Forg Diffuser with five baseline methods on six benchmark datasets. |
| Researcher Affiliation | Academia | 1Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, China 2Southeast Digital Economy Development Institute, China 3Key Laboratory of Interactive Technology and Experience System, Ministry of Culture and Tourism (BUPT), China EMAIL |
| Pseudocode | No | The paper describes the proposed method, Forg Diffuser, using textual explanations and diagrams (Figure 2 and Figure 3) rather than formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Forg Diffuser is evaluated on widely used forgery image datasets: CASIA1 [Dong et al., 2013], DID [Wu and Zhou, 2021], IMD [Novozamsky et al., 2020], Auto [Jia et al., 2023], BSN and RLS26K [Hao et al., 2024]. |
| Dataset Splits | Yes | We divide the above datasets into train and test sets in the ratio of 9:1 for experimentation. |
| Hardware Specification | Yes | We implemented Forg Diffuser based on the Py Torch with one NVIDIA L20 with 48 GB memory for training and inference. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version or any other software dependencies with their version numbers. |
| Experiment Setup | Yes | We trained 100 epochs with batch sizes of 16. AGM is initialized using PVTv2-B4, and the input images are resized to 352 352. The Adam W optimizer is employed, and the initial learning rate is set to 0.001. λ1 and λ2 in the loss function of Equation 12 are set to 0.8 and 0.2, respectively. The time step T is set to 10 for sampling. |