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..
A New Benchmark and Model for Challenging Image Manipulation Detection
Authors: Zhenfei Zhang, Mingyang Li, Ming-Ching Chang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the CIMD benchmark show that our model significantly outperforms So TA IMD methods on CIMD. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University at Albany, State University of New York, New York, USA, 12222 2Department of Bioengineering, Mc Gill University, Montreal, QC, Canada, H3A 0E9 |
| Pseudocode | No | The paper describes the method using text and mathematical equations, but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The dataset is available at: https://github.com/Zhenfei Z/CIMD. The paper states the dataset is available at this link, but does not explicitly state that the source code for the methodology is also provided there or elsewhere. |
| Open Datasets | Yes | To investigate the State-of-The-Art (So TA) IMD methods in those challenging conditions, we introduce a new Challenging Image Manipulation Detection (CIMD) benchmark dataset, which consists of two subsets... The dataset is available at: https://github.com/Zhenfei Z/CIMD. |
| Dataset Splits | No | The training datasets used in this study were adopted from (Kwon et al. 2022). The testing phase entailed the utilization of CIMD-R and CIMD-C to evaluate the efficacy of image-editing-based and compression-based methods, respectively. However, specific training/validation/test splits for these datasets are not provided. |
| Hardware Specification | Yes | Our model was implemented using Py Torch (Paszke et al. 2019) and trained on 8 RTX 2080 GPUs, with batch size 4. |
| Software Dependencies | Yes | Our model was implemented using Py Torch (Paszke et al. 2019) |
| Experiment Setup | Yes | Our model was implemented using Py Torch (Paszke et al. 2019) and trained on 8 RTX 2080 GPUs, with batch size 4. We set the initial learning rate as 0.001 with exponential decay. The training process consists of 250 epochs. |