Learnable Frequency Decomposition for Image Forgery Detection and Localization
Authors: Dong Li, Jiayíng Zhu, Yidi Liu, Xin Lu, Xueyang Fu, Jiawei Liu, Aiping Liu, Zheng-Jun Zha
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art image forgery detection and localization techniques both qualitatively and quantitatively. We conduct extensive experiments on multiple benchmarks and demonstrate that our method outperforms state-of-the-art methods both qualitatively and quantitatively. |
| Researcher Affiliation | Academia | University of Science and Technology of China EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using narrative text, equations, and diagrams, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the code or a link to a code repository. |
| Open Datasets | Yes | Testing Datasets Following [Liu et al., 2022; Wang et al., 2022a], we evaluate our model on CASIA [Dong et al., 2013], Coverage [Wen et al., 2016], Columbia [Hsu and Chang, 2006], NIST16 [Guan et al., 2019] and IMD20 [Novozamsky et al., 2020]. |
| Dataset Splits | Yes | We apply the same training/testing splits as [Hu et al., 2020; Wang et al., 2022a] to fine-tune our model for fair comparisons. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory amounts used for experiments. |
| Software Dependencies | No | The paper mentions using FFT and ResNet-50 pretrained on ImageNet but does not specify version numbers for any software libraries (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | In practice, α is set as 0.60 and β is set as 0.2. We use Res Net-50 pretrained on Image Net [Deng et al., 2009] as the backbone network of the spectral decomposition subnetwork. Pre-training Data We create a sizable image tampering dataset and use it to pre-train our model. This dataset includes three categories: 1) splicing, 2) copy-move, and 3) removal. |