Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning
Authors: Quan Zhang, Yuxin Qi, Xi Tang, Jinwei Fang, Xi Lin, Ke Zhang, Chun Yuan
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results from five datasets (CASIA, Columbia, Coverage, IMD2020, and NIST16) validate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1Tsinghua University 2Shanghai Jiao Tong University 3University of Science and Technology of China |
| Pseudocode | No | The paper describes the method in prose and equations (e.g., equations 1-19) but does not include a dedicated pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide a direct link to a code repository or an explicit statement about the release of source code for the methodology. |
| Open Datasets | Yes | Our method is trained only on the CASIAv2 dataset Dong et al. (2013). For in-distribution (IND) evaluation, we use the CASIAv1 dataset Dong et al. (2013). For out-of-distribution (OOD) evaluation, we use three datasets: Columbia Hsu & Chang (2006), Coverage Wen et al. (2016)and IMD2020 Novozamsky et al. (2020). [...] In order to directly compare with state-of-the-art technologies, we trained on CASIAv2 Dong et al. (2013) and conducted extensive testing on COVER Wen et al. (2016), Columbia Hsu & Chang (2006), NIST16 Hsu & Chang (2006), CASIAv1 Dong et al. (2013), and the recent IMD Novozamsky et al. (2020). |
| Dataset Splits | Yes | Our method is trained only on the CASIAv2 dataset Dong et al. (2013). For in-distribution (IND) evaluation, we use the CASIAv1 dataset Dong et al. (2013). For out-of-distribution (OOD) evaluation, we use three datasets: Columbia Hsu & Chang (2006), Coverage Wen et al. (2016)and IMD2020 Novozamsky et al. (2020). [...] Table 7: Details of the training set and five test sets used in our experiments. [...] Our model was trained on the CASIAv2 dataset and evaluated across all test sets. |
| Hardware Specification | Yes | All experiments are run on NVIDIA A6000 GPUs. |
| Software Dependencies | No | The paper describes algorithms and optimizers used (e.g., Adam W optimizer, Focal Loss) but does not provide specific version numbers for software dependencies like programming languages or libraries. |
| Experiment Setup | Yes | For the optimization process, we train our model using the Adam W optimizer with an initial learning rate of 1e-4. We use a batch size of 4 and train for 100 epochs. We implement a linear warm-up strategy with a cosine annealing scheduler Loshchilov & Hutter (2016) to decay the learning rate. [...] As shown in Figure 7, we conducted hyperparameter analysis on λ1,λ2, and λ3, ultimately selecting the optimal parameter configuration: λ1 = 1.0, λ2 = 0.1, λ3 = 1.0. |